{"id":125764,"date":"2026-05-08T08:42:23","date_gmt":"2026-05-08T08:42:23","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=125764"},"modified":"2026-05-08T08:42:26","modified_gmt":"2026-05-08T08:42:26","slug":"current-amazon-dea-c01-dumps-v12-02-are-available-for-your-certification-continue-to-read-dea-c01-free-dumps-part-2-q41-q90-today","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/current-amazon-dea-c01-dumps-v12-02-are-available-for-your-certification-continue-to-read-dea-c01-free-dumps-part-2-q41-q90-today.html","title":{"rendered":"Current Amazon DEA-C01 Dumps (V12.02) Are Available for Your Certification: Continue to Read DEA-C01 Free Dumps (Part 2, Q41-Q90) Today"},"content":{"rendered":"\n<p>You can trust the Amazon DEA-C01 dumps (V12.02) from DumpsBase. We provide a streamlined and effective preparation experience with valid exam questions and answers, ensuring your success in the AWS Certified Data Engineer &#8211; Associate (DEA-C01) exam. All the 231 practice questions and answers in the V12.02 are curated by subject matter experts to ensure that the questions and answers align with the most current exam objectives set by Amazon. You can access them by reading our <strong><em><a href=\"https:\/\/www.dumpsbase.com\/freedumps\/updated-aws-certified-data-engineer-associate-dea-c01-exam-guide-v12-02-check-dea-c02-free-dumps-part-1-q1-q40-first.html\">Amazon DEA-C01 free dumps (Part 1, Q1-Q40) of V12.02<\/a><\/em><\/strong> first. Then, by utilizing these DEA-C01 exam dumps (V12.02), you can identify knowledge gaps, improve your technical capacities, and gain the confidence needed to clear the AWS Certified Data Engineer &#8211; Associate exam on your first attempt. Today, we will continue to share more free demos online, and you can explore them to evaluate the quality and depth of the content before committing to the full package.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Below are the Amazon DEA-C01 free dumps (Part 2, Q41-Q90) of V12.02:<\/h2>\n\n\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam11693\" style=\"display:none;text-align:center;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\"><\/p>\n\n<div class=\"watupro-exam-description\" id=\"description-quiz-11693\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11693\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-458756'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint.<br \/>\r\n<br \/>\r\nThe data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.<br \/>\r\n<br \/>\r\nWhich solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='458756' \/><input type='hidden' id='answerType458756' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458756[]' id='answer-id-1773405' class='answer   answerof-458756 ' value='1773405'   \/><label for='answer-id-1773405' id='answer-label-1773405' class=' answer'><span>Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458756[]' id='answer-id-1773406' class='answer   answerof-458756 ' value='1773406'   \/><label for='answer-id-1773406' id='answer-label-1773406' class=' answer'><span>Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458756[]' id='answer-id-1773407' class='answer   answerof-458756 ' value='1773407'   \/><label for='answer-id-1773407' id='answer-label-1773407' class=' answer'><span>Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458756[]' id='answer-id-1773408' class='answer   answerof-458756 ' value='1773408'   \/><label for='answer-id-1773408' id='answer-label-1773408' class=' answer'><span>Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-2' style=';'><div id='questionWrap-2'  class='   watupro-question-id-458757'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='458757' \/><input type='hidden' id='answerType458757' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458757[]' id='answer-id-1773409' class='answer   answerof-458757 ' value='1773409'   \/><label for='answer-id-1773409' id='answer-label-1773409' class=' answer'><span>Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458757[]' id='answer-id-1773410' class='answer   answerof-458757 ' value='1773410'   \/><label for='answer-id-1773410' id='answer-label-1773410' class=' answer'><span>Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458757[]' id='answer-id-1773411' class='answer   answerof-458757 ' value='1773411'   \/><label for='answer-id-1773411' id='answer-label-1773411' class=' answer'><span>Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458757[]' id='answer-id-1773412' class='answer   answerof-458757 ' value='1773412'   \/><label for='answer-id-1773412' id='answer-label-1773412' class=' answer'><span>Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-3' style=';'><div id='questionWrap-3'  class='   watupro-question-id-458758'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access. <br \/>\r<br>Which solution will meet these requirements with the LEAST effort?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='458758' \/><input type='hidden' id='answerType458758' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458758[]' id='answer-id-1773413' class='answer   answerof-458758 ' value='1773413'   \/><label for='answer-id-1773413' id='answer-label-1773413' class=' answer'><span>Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458758[]' id='answer-id-1773414' class='answer   answerof-458758 ' value='1773414'   \/><label for='answer-id-1773414' id='answer-label-1773414' class=' answer'><span>Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458758[]' id='answer-id-1773415' class='answer   answerof-458758 ' value='1773415'   \/><label for='answer-id-1773415' id='answer-label-1773415' class=' answer'><span>Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458758[]' id='answer-id-1773416' class='answer   answerof-458758 ' value='1773416'   \/><label for='answer-id-1773416' id='answer-label-1773416' class=' answer'><span>Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-4' style=';'><div id='questionWrap-4'  class='   watupro-question-id-458759'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance. <br \/>\r<br>Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_4' value='458759' \/><input type='hidden' id='answerType458759' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458759[]' id='answer-id-1773417' class='answer   answerof-458759 ' value='1773417'   \/><label for='answer-id-1773417' id='answer-label-1773417' class=' answer'><span>Use Hadoop Distributed File System (HDFS) as a persistent data store.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458759[]' id='answer-id-1773418' class='answer   answerof-458759 ' value='1773418'   \/><label for='answer-id-1773418' id='answer-label-1773418' class=' answer'><span>Use Amazon S3 as a persistent data store.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458759[]' id='answer-id-1773419' class='answer   answerof-458759 ' value='1773419'   \/><label for='answer-id-1773419' id='answer-label-1773419' class=' answer'><span>Use x86-based instances for core nodes and task nodes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458759[]' id='answer-id-1773420' class='answer   answerof-458759 ' value='1773420'   \/><label for='answer-id-1773420' id='answer-label-1773420' class=' answer'><span>Use Graviton instances for core nodes and task nodes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458759[]' id='answer-id-1773421' class='answer   answerof-458759 ' value='1773421'   \/><label for='answer-id-1773421' id='answer-label-1773421' class=' answer'><span>Use Spot Instances for all primary nodes.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-5' style=';'><div id='questionWrap-5'  class='   watupro-question-id-458760'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes. <br \/>\r<br>Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_5' value='458760' \/><input type='hidden' id='answerType458760' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458760[]' id='answer-id-1773422' class='answer   answerof-458760 ' value='1773422'   \/><label for='answer-id-1773422' id='answer-label-1773422' class=' answer'><span>Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458760[]' id='answer-id-1773423' class='answer   answerof-458760 ' value='1773423'   \/><label for='answer-id-1773423' id='answer-label-1773423' class=' answer'><span>Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458760[]' id='answer-id-1773424' class='answer   answerof-458760 ' value='1773424'   \/><label for='answer-id-1773424' id='answer-label-1773424' class=' answer'><span>Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458760[]' id='answer-id-1773425' class='answer   answerof-458760 ' value='1773425'   \/><label for='answer-id-1773425' id='answer-label-1773425' class=' answer'><span>Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458760[]' id='answer-id-1773426' class='answer   answerof-458760 ' value='1773426'   \/><label for='answer-id-1773426' id='answer-label-1773426' class=' answer'><span>Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-6' style=';'><div id='questionWrap-6'  class='   watupro-question-id-458761'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently. <br \/>\r<br>The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database. <br \/>\r<br>Which AWS service should the company use to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='458761' \/><input type='hidden' id='answerType458761' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458761[]' id='answer-id-1773427' class='answer   answerof-458761 ' value='1773427'   \/><label for='answer-id-1773427' id='answer-label-1773427' class=' answer'><span>AWS Lambda<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458761[]' id='answer-id-1773428' class='answer   answerof-458761 ' value='1773428'   \/><label for='answer-id-1773428' id='answer-label-1773428' class=' answer'><span>AWS Database Migration Service (AWS DMS)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458761[]' id='answer-id-1773429' class='answer   answerof-458761 ' value='1773429'   \/><label for='answer-id-1773429' id='answer-label-1773429' class=' answer'><span>AWS Direct Connect<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458761[]' id='answer-id-1773430' class='answer   answerof-458761 ' value='1773430'   \/><label for='answer-id-1773430' id='answer-label-1773430' class=' answer'><span>AWS DataSync<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-7' style=';'><div id='questionWrap-7'  class='   watupro-question-id-458762'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.<br \/>\r\n<br \/>\r\nA data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.<br \/>\r\n<br \/>\r\nWhich combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_7' value='458762' \/><input type='hidden' id='answerType458762' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458762[]' id='answer-id-1773431' class='answer   answerof-458762 ' value='1773431'   \/><label for='answer-id-1773431' id='answer-label-1773431' class=' answer'><span>Use data filters for each Region to register the S3 paths as data locations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458762[]' id='answer-id-1850993' class='answer   answerof-458762 ' value='1850993'   \/><label for='answer-id-1850993' id='answer-label-1850993' class=' answer'><span>Register the S3 path as an AWS Lake Formation location.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458762[]' id='answer-id-1850994' class='answer   answerof-458762 ' value='1850994'   \/><label for='answer-id-1850994' id='answer-label-1850994' class=' answer'><span>Modify the IAM roles of the HR departments to add a data filter for each department's Region.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458762[]' id='answer-id-1850995' class='answer   answerof-458762 ' value='1850995'   \/><label for='answer-id-1850995' id='answer-label-1850995' class=' answer'><span>Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458762[]' id='answer-id-1850996' class='answer   answerof-458762 ' value='1850996'   \/><label for='answer-id-1850996' id='answer-label-1850996' class=' answer'><span>Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-8' style=';'><div id='questionWrap-8'  class='   watupro-question-id-458763'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script. <br \/>\r<br>A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials. <br \/>\r<br>Which combination of steps should the data engineer take to meet these requirements? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_8' value='458763' \/><input type='hidden' id='answerType458763' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458763[]' id='answer-id-1773432' class='answer   answerof-458763 ' value='1773432'   \/><label for='answer-id-1773432' id='answer-label-1773432' class=' answer'><span>Store the credentials in the AWS Glue job parameters.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458763[]' id='answer-id-1773433' class='answer   answerof-458763 ' value='1773433'   \/><label for='answer-id-1773433' id='answer-label-1773433' class=' answer'><span>Store the credentials in a configuration file that is in an Amazon S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458763[]' id='answer-id-1773434' class='answer   answerof-458763 ' value='1773434'   \/><label for='answer-id-1773434' id='answer-label-1773434' class=' answer'><span>Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458763[]' id='answer-id-1773435' class='answer   answerof-458763 ' value='1773435'   \/><label for='answer-id-1773435' id='answer-label-1773435' class=' answer'><span>Store the credentials in AWS Secrets Manager.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458763[]' id='answer-id-1773436' class='answer   answerof-458763 ' value='1773436'   \/><label for='answer-id-1773436' id='answer-label-1773436' class=' answer'><span>Grant the AWS Glue job 1AM role access to the stored credentials.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-9' style=';'><div id='questionWrap-9'  class='   watupro-question-id-458764'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.<br \/>\r\n<br \/>\r\nThe developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.<br \/>\r\n<br \/>\r\nWhich combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_9' value='458764' \/><input type='hidden' id='answerType458764' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458764[]' id='answer-id-1773437' class='answer   answerof-458764 ' value='1773437'   \/><label for='answer-id-1773437' id='answer-label-1773437' class=' answer'><span>Turn on the public access setting for the DB instance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458764[]' id='answer-id-1850997' class='answer   answerof-458764 ' value='1850997'   \/><label for='answer-id-1850997' id='answer-label-1850997' class=' answer'><span>Update the security group of the DB instance to allow only Lambda function invocations on the database port.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458764[]' id='answer-id-1850998' class='answer   answerof-458764 ' value='1850998'   \/><label for='answer-id-1850998' id='answer-label-1850998' class=' answer'><span>Configure the Lambda function to run in the same subnet that the DB instance uses.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458764[]' id='answer-id-1850999' class='answer   answerof-458764 ' value='1850999'   \/><label for='answer-id-1850999' id='answer-label-1850999' class=' answer'><span>Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458764[]' id='answer-id-1851000' class='answer   answerof-458764 ' value='1851000'   \/><label for='answer-id-1851000' id='answer-label-1851000' class=' answer'><span>Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-10' style=';'><div id='questionWrap-10'  class='   watupro-question-id-458765'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned <br \/>\r<br>capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='458765' \/><input type='hidden' id='answerType458765' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458765[]' id='answer-id-1773438' class='answer   answerof-458765 ' value='1773438'   \/><label for='answer-id-1773438' id='answer-label-1773438' class=' answer'><span>Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458765[]' id='answer-id-1773439' class='answer   answerof-458765 ' value='1773439'   \/><label for='answer-id-1773439' id='answer-label-1773439' class=' answer'><span>Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458765[]' id='answer-id-1773440' class='answer   answerof-458765 ' value='1773440'   \/><label for='answer-id-1773440' id='answer-label-1773440' class=' answer'><span>Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458765[]' id='answer-id-1773441' class='answer   answerof-458765 ' value='1773441'   \/><label for='answer-id-1773441' id='answer-label-1773441' class=' answer'><span>Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-11' style=';'><div id='questionWrap-11'  class='   watupro-question-id-458766'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline. <br \/>\r<br>Which AWS service or feature will meet these requirements MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='458766' \/><input type='hidden' id='answerType458766' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458766[]' id='answer-id-1773442' class='answer   answerof-458766 ' value='1773442'   \/><label for='answer-id-1773442' id='answer-label-1773442' class=' answer'><span>AWS Step Functions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458766[]' id='answer-id-1773443' class='answer   answerof-458766 ' value='1773443'   \/><label for='answer-id-1773443' id='answer-label-1773443' class=' answer'><span>AWS Glue workflows<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458766[]' id='answer-id-1773444' class='answer   answerof-458766 ' value='1773444'   \/><label for='answer-id-1773444' id='answer-label-1773444' class=' answer'><span>AWS Glue Studio<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458766[]' id='answer-id-1773445' class='answer   answerof-458766 ' value='1773445'   \/><label for='answer-id-1773445' id='answer-label-1773445' class=' answer'><span>Amazon Managed Workflows for Apache Airflow (Amazon MWAA)<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-12' style=';'><div id='questionWrap-12'  class='   watupro-question-id-458767'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='458767' \/><input type='hidden' id='answerType458767' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458767[]' id='answer-id-1773446' class='answer   answerof-458767 ' value='1773446'   \/><label for='answer-id-1773446' id='answer-label-1773446' class=' answer'><span>Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe-Write a SQL SELECT statement on the dataframe to query the required column.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458767[]' id='answer-id-1773447' class='answer   answerof-458767 ' value='1773447'   \/><label for='answer-id-1773447' id='answer-label-1773447' class=' answer'><span>Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458767[]' id='answer-id-1773448' class='answer   answerof-458767 ' value='1773448'   \/><label for='answer-id-1773448' id='answer-label-1773448' class=' answer'><span>Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458767[]' id='answer-id-1773449' class='answer   answerof-458767 ' value='1773449'   \/><label for='answer-id-1773449' id='answer-label-1773449' class=' answer'><span>Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-13' style=';'><div id='questionWrap-13'  class='   watupro-question-id-458768'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='458768' \/><input type='hidden' id='answerType458768' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458768[]' id='answer-id-1773450' class='answer   answerof-458768 ' value='1773450'   \/><label for='answer-id-1773450' id='answer-label-1773450' class=' answer'><span>Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458768[]' id='answer-id-1773451' class='answer   answerof-458768 ' value='1773451'   \/><label for='answer-id-1773451' id='answer-label-1773451' class=' answer'><span>Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458768[]' id='answer-id-1773452' class='answer   answerof-458768 ' value='1773452'   \/><label for='answer-id-1773452' id='answer-label-1773452' class=' answer'><span>Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458768[]' id='answer-id-1773453' class='answer   answerof-458768 ' value='1773453'   \/><label for='answer-id-1773453' id='answer-label-1773453' class=' answer'><span>Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-14' style=';'><div id='questionWrap-14'  class='   watupro-question-id-458769'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII. <br \/>\r<br>To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset. <br \/>\r<br>Which solution will meet the requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='458769' \/><input type='hidden' id='answerType458769' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458769[]' id='answer-id-1773454' class='answer   answerof-458769 ' value='1773454'   \/><label for='answer-id-1773454' id='answer-label-1773454' class=' answer'><span>Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458769[]' id='answer-id-1773455' class='answer   answerof-458769 ' value='1773455'   \/><label for='answer-id-1773455' id='answer-label-1773455' class=' answer'><span>Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458769[]' id='answer-id-1773456' class='answer   answerof-458769 ' value='1773456'   \/><label for='answer-id-1773456' id='answer-label-1773456' class=' answer'><span>Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458769[]' id='answer-id-1773457' class='answer   answerof-458769 ' value='1773457'   \/><label for='answer-id-1773457' id='answer-label-1773457' class=' answer'><span>Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-15' style=';'><div id='questionWrap-15'  class='   watupro-question-id-458770'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake. <br \/>\r<br>The company needs to identify matching records even when the records do not have a common unique identifier. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='458770' \/><input type='hidden' id='answerType458770' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458770[]' id='answer-id-1773458' class='answer   answerof-458770 ' value='1773458'   \/><label for='answer-id-1773458' id='answer-label-1773458' class=' answer'><span>Use Amazon Made pattern matching as part of the ETL job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458770[]' id='answer-id-1773459' class='answer   answerof-458770 ' value='1773459'   \/><label for='answer-id-1773459' id='answer-label-1773459' class=' answer'><span>Train and use the AWS Glue PySpark Filter class in the ETL job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458770[]' id='answer-id-1773460' class='answer   answerof-458770 ' value='1773460'   \/><label for='answer-id-1773460' id='answer-label-1773460' class=' answer'><span>Partition tables and use the ETL job to partition the data on a unique identifier.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458770[]' id='answer-id-1773461' class='answer   answerof-458770 ' value='1773461'   \/><label for='answer-id-1773461' id='answer-label-1773461' class=' answer'><span>Train and use the AWS Lake Formation FindMatches transform in the ETL job.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-16' style=';'><div id='questionWrap-16'  class='   watupro-question-id-458771'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.<br \/>\r\n<br \/>\r\nThe company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.<br \/>\r\n<br \/>\r\nThe company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.<br \/>\r\n<br \/>\r\nWhich combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_16' value='458771' \/><input type='hidden' id='answerType458771' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458771[]' id='answer-id-1773462' class='answer   answerof-458771 ' value='1773462'   \/><label for='answer-id-1773462' id='answer-label-1773462' class=' answer'><span>Configure the third-party application to create the files in a columnar format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458771[]' id='answer-id-1851001' class='answer   answerof-458771 ' value='1851001'   \/><label for='answer-id-1851001' id='answer-label-1851001' class=' answer'><span>Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458771[]' id='answer-id-1851002' class='answer   answerof-458771 ' value='1851002'   \/><label for='answer-id-1851002' id='answer-label-1851002' class=' answer'><span>Partition the order data in the S3 bucket based on order date.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458771[]' id='answer-id-1851003' class='answer   answerof-458771 ' value='1851003'   \/><label for='answer-id-1851003' id='answer-label-1851003' class=' answer'><span>Configure the third-party application to create the files in JSON format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458771[]' id='answer-id-1851004' class='answer   answerof-458771 ' value='1851004'   \/><label for='answer-id-1851004' id='answer-label-1851004' class=' answer'><span>Load the JSON data into the Amazon Redshift table in a SUPER type column.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-17' style=';'><div id='questionWrap-17'  class='   watupro-question-id-458772'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table. <br \/>\r<br>The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time. <br \/>\r<br>Which solutions will meet these requirements? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_17' value='458772' \/><input type='hidden' id='answerType458772' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458772[]' id='answer-id-1773463' class='answer   answerof-458772 ' value='1773463'   \/><label for='answer-id-1773463' id='answer-label-1773463' class=' answer'><span>Create an AWS Glue partition index. Enable partition filtering.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458772[]' id='answer-id-1773464' class='answer   answerof-458772 ' value='1773464'   \/><label for='answer-id-1773464' id='answer-label-1773464' class=' answer'><span>Bucket the data based on a column that the data have in common in a WHERE clause of the user query<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458772[]' id='answer-id-1773465' class='answer   answerof-458772 ' value='1773465'   \/><label for='answer-id-1773465' id='answer-label-1773465' class=' answer'><span>Use Athena partition projection based on the S3 bucket prefix.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458772[]' id='answer-id-1773466' class='answer   answerof-458772 ' value='1773466'   \/><label for='answer-id-1773466' id='answer-label-1773466' class=' answer'><span>Transform the data that is in the S3 bucket to Apache Parquet format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458772[]' id='answer-id-1773467' class='answer   answerof-458772 ' value='1773467'   \/><label for='answer-id-1773467' id='answer-label-1773467' class=' answer'><span>Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-18' style=';'><div id='questionWrap-18'  class='   watupro-question-id-458773'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. <br \/>\r<br>The company wants to minimize the effort and time required to incorporate third-party datasets. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='458773' \/><input type='hidden' id='answerType458773' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458773[]' id='answer-id-1773468' class='answer   answerof-458773 ' value='1773468'   \/><label for='answer-id-1773468' id='answer-label-1773468' class=' answer'><span>Use API calls to access and integrate third-party datasets from AWS Data Exchange.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458773[]' id='answer-id-1773469' class='answer   answerof-458773 ' value='1773469'   \/><label for='answer-id-1773469' id='answer-label-1773469' class=' answer'><span>Use API calls to access and integrate third-party datasets from AWS<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458773[]' id='answer-id-1773470' class='answer   answerof-458773 ' value='1773470'   \/><label for='answer-id-1773470' id='answer-label-1773470' class=' answer'><span>Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS Code Commit repositories.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458773[]' id='answer-id-1773471' class='answer   answerof-458773 ' value='1773471'   \/><label for='answer-id-1773471' id='answer-label-1773471' class=' answer'><span>Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-19' style=';'><div id='questionWrap-19'  class='   watupro-question-id-458774'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated. <br \/>\r<br>A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='458774' \/><input type='hidden' id='answerType458774' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458774[]' id='answer-id-1773472' class='answer   answerof-458774 ' value='1773472'   \/><label for='answer-id-1773472' id='answer-label-1773472' class=' answer'><span>Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458774[]' id='answer-id-1773473' class='answer   answerof-458774 ' value='1773473'   \/><label for='answer-id-1773473' id='answer-label-1773473' class=' answer'><span>Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458774[]' id='answer-id-1773474' class='answer   answerof-458774 ' value='1773474'   \/><label for='answer-id-1773474' id='answer-label-1773474' class=' answer'><span>Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458774[]' id='answer-id-1773475' class='answer   answerof-458774 ' value='1773475'   \/><label for='answer-id-1773475' id='answer-label-1773475' class=' answer'><span>Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-20' style=';'><div id='questionWrap-20'  class='   watupro-question-id-458775'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day. <br \/>\r<br>A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs. <br \/>\r<br>Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_20' value='458775' \/><input type='hidden' id='answerType458775' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458775[]' id='answer-id-1773476' class='answer   answerof-458775 ' value='1773476'   \/><label for='answer-id-1773476' id='answer-label-1773476' class=' answer'><span>Partition the data that is in the S3 bucket. Organize the data by year, month, and day.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458775[]' id='answer-id-1773477' class='answer   answerof-458775 ' value='1773477'   \/><label for='answer-id-1773477' id='answer-label-1773477' class=' answer'><span>Increase the AWS Glue instance size by scaling up the worker type.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458775[]' id='answer-id-1773478' class='answer   answerof-458775 ' value='1773478'   \/><label for='answer-id-1773478' id='answer-label-1773478' class=' answer'><span>Convert the AWS Glue schema to the DynamicFrame schema class.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458775[]' id='answer-id-1773479' class='answer   answerof-458775 ' value='1773479'   \/><label for='answer-id-1773479' id='answer-label-1773479' class=' answer'><span>Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458775[]' id='answer-id-1773480' class='answer   answerof-458775 ' value='1773480'   \/><label for='answer-id-1773480' id='answer-label-1773480' class=' answer'><span>Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-21' style=';'><div id='questionWrap-21'  class='   watupro-question-id-458776'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information. <br \/>\r<br>The data engineer must identify and remove duplicate information from the legacy application data. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='458776' \/><input type='hidden' id='answerType458776' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458776[]' id='answer-id-1773481' class='answer   answerof-458776 ' value='1773481'   \/><label for='answer-id-1773481' id='answer-label-1773481' class=' answer'><span>Write a custom extract, transform, and load (ETL) job in Python. Use the Data Frame drop duplicatesf) function by importing the Pandas library to perform data deduplication.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458776[]' id='answer-id-1773482' class='answer   answerof-458776 ' value='1773482'   \/><label for='answer-id-1773482' id='answer-label-1773482' class=' answer'><span>Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458776[]' id='answer-id-1773483' class='answer   answerof-458776 ' value='1773483'   \/><label for='answer-id-1773483' id='answer-label-1773483' class=' answer'><span>Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. \r\nUse the dedupe library to perform data deduplication.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458776[]' id='answer-id-1773484' class='answer   answerof-458776 ' value='1773484'   \/><label for='answer-id-1773484' id='answer-label-1773484' class=' answer'><span>Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-22' style=';'><div id='questionWrap-22'  class='   watupro-question-id-458777'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour. <br \/>\r<br>Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_22' value='458777' \/><input type='hidden' id='answerType458777' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458777[]' id='answer-id-1773485' class='answer   answerof-458777 ' value='1773485'   \/><label for='answer-id-1773485' id='answer-label-1773485' class=' answer'><span>Configure AWS Glue triggers to run the ETL jobs even\/ hour.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458777[]' id='answer-id-1773486' class='answer   answerof-458777 ' value='1773486'   \/><label for='answer-id-1773486' id='answer-label-1773486' class=' answer'><span>Use AWS Glue DataBrewto clean and prepare the data for analytics.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458777[]' id='answer-id-1773487' class='answer   answerof-458777 ' value='1773487'   \/><label for='answer-id-1773487' id='answer-label-1773487' class=' answer'><span>Use AWS Lambda functions to schedule and run the ETL jobs even\/ hour.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458777[]' id='answer-id-1773488' class='answer   answerof-458777 ' value='1773488'   \/><label for='answer-id-1773488' id='answer-label-1773488' class=' answer'><span>Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458777[]' id='answer-id-1773489' class='answer   answerof-458777 ' value='1773489'   \/><label for='answer-id-1773489' id='answer-label-1773489' class=' answer'><span>Use the Redshift Data API to load transformed data into Amazon Redshift.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-23' style=';'><div id='questionWrap-23'  class='   watupro-question-id-458778'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted. <br \/>\r<br>The data engineer needs a solution that will prevent unintentional file deletion in the future. <br \/>\r<br>Which solution will meet this requirement with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='458778' \/><input type='hidden' id='answerType458778' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458778[]' id='answer-id-1773490' class='answer   answerof-458778 ' value='1773490'   \/><label for='answer-id-1773490' id='answer-label-1773490' class=' answer'><span>Manually back up the S3 bucket on a regular basis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458778[]' id='answer-id-1773491' class='answer   answerof-458778 ' value='1773491'   \/><label for='answer-id-1773491' id='answer-label-1773491' class=' answer'><span>Enable S3 Versioning for the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458778[]' id='answer-id-1773492' class='answer   answerof-458778 ' value='1773492'   \/><label for='answer-id-1773492' id='answer-label-1773492' class=' answer'><span>Configure replication for the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458778[]' id='answer-id-1773493' class='answer   answerof-458778 ' value='1773493'   \/><label for='answer-id-1773493' id='answer-label-1773493' class=' answer'><span>Use an Amazon S3 Glacier storage class to archive the data that is in the S3 bucket.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-24' style=';'><div id='questionWrap-24'  class='   watupro-question-id-458779'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='458779' \/><input type='hidden' id='answerType458779' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458779[]' id='answer-id-1773494' class='answer   answerof-458779 ' value='1773494'   \/><label for='answer-id-1773494' id='answer-label-1773494' class=' answer'><span>Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458779[]' id='answer-id-1773495' class='answer   answerof-458779 ' value='1773495'   \/><label for='answer-id-1773495' id='answer-label-1773495' class=' answer'><span>Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458779[]' id='answer-id-1773496' class='answer   answerof-458779 ' value='1773496'   \/><label for='answer-id-1773496' id='answer-label-1773496' class=' answer'><span>Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458779[]' id='answer-id-1773497' class='answer   answerof-458779 ' value='1773497'   \/><label for='answer-id-1773497' id='answer-label-1773497' class=' answer'><span>Turn on concurrency scaling for the daily usage quota for the Redshift cluster.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-25' style=';'><div id='questionWrap-25'  class='   watupro-question-id-458780'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml<br \/>\r\n<br \/>\r\nfiles that are stored in Amazon S3.<br \/>\r\n<br \/>\r\nThe company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.<br \/>\r\n<br \/>\r\nWhich solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='458780' \/><input type='hidden' id='answerType458780' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458780[]' id='answer-id-1773498' class='answer   answerof-458780 ' value='1773498'   \/><label for='answer-id-1773498' id='answer-label-1773498' class=' answer'><span>Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458780[]' id='answer-id-1851005' class='answer   answerof-458780 ' value='1851005'   \/><label for='answer-id-1851005' id='answer-label-1851005' class=' answer'><span>Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458780[]' id='answer-id-1851006' class='answer   answerof-458780 ' value='1851006'   \/><label for='answer-id-1851006' id='answer-label-1851006' class=' answer'><span>Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458780[]' id='answer-id-1851007' class='answer   answerof-458780 ' value='1851007'   \/><label for='answer-id-1851007' id='answer-label-1851007' class=' answer'><span>Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-26' style=';'><div id='questionWrap-26'  class='   watupro-question-id-458781'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently. <br \/>\r<br>The company needs to cost-optimize its Amazon S3 storage. <br \/>\r<br>Which solution will meet these requirements MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='458781' \/><input type='hidden' id='answerType458781' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458781[]' id='answer-id-1773499' class='answer   answerof-458781 ' value='1773499'   \/><label for='answer-id-1773499' id='answer-label-1773499' class=' answer'><span>Apply a lifecycle policy to transition records to S3 Standard Infrequent-Access (S3 Standard-IA) storage after 30 days.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458781[]' id='answer-id-1773500' class='answer   answerof-458781 ' value='1773500'   \/><label for='answer-id-1773500' id='answer-label-1773500' class=' answer'><span>Use S3 Intelligent-Tiering storage.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458781[]' id='answer-id-1773501' class='answer   answerof-458781 ' value='1773501'   \/><label for='answer-id-1773501' id='answer-label-1773501' class=' answer'><span>Transition records to S3 Glacier Deep Archive storage after 30 days.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458781[]' id='answer-id-1773502' class='answer   answerof-458781 ' value='1773502'   \/><label for='answer-id-1773502' id='answer-label-1773502' class=' answer'><span>Use S3 Standard-Infrequent Access (S3 Standard-IA) storage for all customer records.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-27' style=';'><div id='questionWrap-27'  class='   watupro-question-id-458782'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.<br \/>\r\n<br \/>\r\nThe company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.<br \/>\r\n<br \/>\r\nWhich Amazon Redshift command will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='458782' \/><input type='hidden' id='answerType458782' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458782[]' id='answer-id-1773503' class='answer   answerof-458782 ' value='1773503'   \/><label for='answer-id-1773503' id='answer-label-1773503' class=' answer'><span>VACUUM FULL Orders<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458782[]' id='answer-id-1851008' class='answer   answerof-458782 ' value='1851008'   \/><label for='answer-id-1851008' id='answer-label-1851008' class=' answer'><span>VACUUM DELETE ONLY Orders <\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458782[]' id='answer-id-1851009' class='answer   answerof-458782 ' value='1851009'   \/><label for='answer-id-1851009' id='answer-label-1851009' class=' answer'><span>VACUUM REINDEX Orders<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458782[]' id='answer-id-1851010' class='answer   answerof-458782 ' value='1851010'   \/><label for='answer-id-1851010' id='answer-label-1851010' class=' answer'><span>VACUUM SORT ONLY Orders<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-28' style=';'><div id='questionWrap-28'  class='   watupro-question-id-458783'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day. <br \/>\r<br>When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%. <br \/>\r<br>The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job. <br \/>\r<br>Which solution will meet these requirements MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_28' value='458783' \/><input type='hidden' id='answerType458783' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458783[]' id='answer-id-1773504' class='answer   answerof-458783 ' value='1773504'   \/><label for='answer-id-1773504' id='answer-label-1773504' class=' answer'><span>Increase the maximum number of task nodes for EMR managed scaling to 10.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458783[]' id='answer-id-1773505' class='answer   answerof-458783 ' value='1773505'   \/><label for='answer-id-1773505' id='answer-label-1773505' class=' answer'><span>Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458783[]' id='answer-id-1773506' class='answer   answerof-458783 ' value='1773506'   \/><label for='answer-id-1773506' id='answer-label-1773506' class=' answer'><span>Switch the task node type from general purpose EC2 instances to compute optimized EC2 instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458783[]' id='answer-id-1773507' class='answer   answerof-458783 ' value='1773507'   \/><label for='answer-id-1773507' id='answer-label-1773507' class=' answer'><span>Reduce the scaling cooldown period for the provisioned EMR cluster.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-29' style=';'><div id='questionWrap-29'  class='   watupro-question-id-458784'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.<br \/>\r\n<br \/>\r\nThe company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.<br \/>\r\n<br \/>\r\nWhich solution will meet these requirements with the LOWEST latency?<\/div><input type='hidden' name='question_id[]' id='qID_29' value='458784' \/><input type='hidden' id='answerType458784' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458784[]' id='answer-id-1773508' class='answer   answerof-458784 ' value='1773508'   \/><label for='answer-id-1773508' id='answer-label-1773508' class=' answer'><span>Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458784[]' id='answer-id-1851011' class='answer   answerof-458784 ' value='1851011'   \/><label for='answer-id-1851011' id='answer-label-1851011' class=' answer'><span>Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458784[]' id='answer-id-1851012' class='answer   answerof-458784 ' value='1851012'   \/><label for='answer-id-1851012' id='answer-label-1851012' class=' answer'><span>Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458784[]' id='answer-id-1851013' class='answer   answerof-458784 ' value='1851013'   \/><label for='answer-id-1851013' id='answer-label-1851013' class=' answer'><span>Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-30' style=';'><div id='questionWrap-30'  class='   watupro-question-id-458785'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII. <br \/>\r<br>Which solution will meet this requirement with the LEAST operational effort?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='458785' \/><input type='hidden' id='answerType458785' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773509' class='answer   answerof-458785 ' value='1773509'   \/><label for='answer-id-1773509' id='answer-label-1773509' class=' answer'><span>Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PI<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773510' class='answer   answerof-458785 ' value='1773510'   \/><label for='answer-id-1773510' id='answer-label-1773510' class=' answer'><span>Use an AWS SDK to obfuscate the PI<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773511' class='answer   answerof-458785 ' value='1773511'   \/><label for='answer-id-1773511' id='answer-label-1773511' class=' answer'><span>Set the S3 data lake as the target for the delivery stream.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773512' class='answer   answerof-458785 ' value='1773512'   \/><label for='answer-id-1773512' id='answer-label-1773512' class=' answer'><span>Use the Detect PII transform in AWS Glue Studio to identify the PI<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773513' class='answer   answerof-458785 ' value='1773513'   \/><label for='answer-id-1773513' id='answer-label-1773513' class=' answer'><span>Obfuscate the PI<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773514' class='answer   answerof-458785 ' value='1773514'   \/><label for='answer-id-1773514' id='answer-label-1773514' class=' answer'><span>Use an AWS \r\nStep Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773515' class='answer   answerof-458785 ' value='1773515'   \/><label for='answer-id-1773515' id='answer-label-1773515' class=' answer'><span>Use the Detect PII transform in AWS Glue Studio to identify the PI<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773516' class='answer   answerof-458785 ' value='1773516'   \/><label for='answer-id-1773516' id='answer-label-1773516' class=' answer'><span>Create a rule in AWS Glue Data Quality to obfuscate the PI<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773517' class='answer   answerof-458785 ' value='1773517'   \/><label for='answer-id-1773517' id='answer-label-1773517' class=' answer'><span>Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773518' class='answer   answerof-458785 ' value='1773518'   \/><label for='answer-id-1773518' id='answer-label-1773518' class=' answer'><span>Ingest the dataset into Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458785[]' id='answer-id-1773519' class='answer   answerof-458785 ' value='1773519'   \/><label for='answer-id-1773519' id='answer-label-1773519' class=' answer'><span>Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-31' style=';'><div id='questionWrap-31'  class='   watupro-question-id-458786'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution. <br \/>\r<br>A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations. <br \/>\r<br>The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_31' value='458786' \/><input type='hidden' id='answerType458786' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458786[]' id='answer-id-1773520' class='answer   answerof-458786 ' value='1773520'   \/><label for='answer-id-1773520' id='answer-label-1773520' class=' answer'><span>Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458786[]' id='answer-id-1773521' class='answer   answerof-458786 ' value='1773521'   \/><label for='answer-id-1773521' id='answer-label-1773521' class=' answer'><span>Change the distribution key to the table column that has the largest dimension.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458786[]' id='answer-id-1773522' class='answer   answerof-458786 ' value='1773522'   \/><label for='answer-id-1773522' id='answer-label-1773522' class=' answer'><span>Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458786[]' id='answer-id-1773523' class='answer   answerof-458786 ' value='1773523'   \/><label for='answer-id-1773523' id='answer-label-1773523' class=' answer'><span>Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-32' style=';'><div id='questionWrap-32'  class='   watupro-question-id-458787'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column. <br \/>\r<br>Which solution will MOST speed up the Athena query performance?<\/div><input type='hidden' name='question_id[]' id='qID_32' value='458787' \/><input type='hidden' id='answerType458787' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458787[]' id='answer-id-1773524' class='answer   answerof-458787 ' value='1773524'   \/><label for='answer-id-1773524' id='answer-label-1773524' class=' answer'><span>Change the data format from .csvto JSON format. Apply Snappy compression.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458787[]' id='answer-id-1773525' class='answer   answerof-458787 ' value='1773525'   \/><label for='answer-id-1773525' id='answer-label-1773525' class=' answer'><span>Compress the .csv files by using Snappy compression.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458787[]' id='answer-id-1773526' class='answer   answerof-458787 ' value='1773526'   \/><label for='answer-id-1773526' id='answer-label-1773526' class=' answer'><span>Change the data format from .csvto Apache Parquet. Apply Snappy compression.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458787[]' id='answer-id-1773527' class='answer   answerof-458787 ' value='1773527'   \/><label for='answer-id-1773527' id='answer-label-1773527' class=' answer'><span>Compress the .csv files by using gzjg compression.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-33' style=';'><div id='questionWrap-33'  class='   watupro-question-id-458788'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time. <br \/>\r<br>Which solution will run the Glue jobs in the MOST cost-effective way?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='458788' \/><input type='hidden' id='answerType458788' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458788[]' id='answer-id-1773528' class='answer   answerof-458788 ' value='1773528'   \/><label for='answer-id-1773528' id='answer-label-1773528' class=' answer'><span>Choose the FLEX execution class in the Glue job properties.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458788[]' id='answer-id-1773529' class='answer   answerof-458788 ' value='1773529'   \/><label for='answer-id-1773529' id='answer-label-1773529' class=' answer'><span>Use the Spot Instance type in Glue job properties.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458788[]' id='answer-id-1773530' class='answer   answerof-458788 ' value='1773530'   \/><label for='answer-id-1773530' id='answer-label-1773530' class=' answer'><span>Choose the STANDARD execution class in the Glue job properties.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458788[]' id='answer-id-1773531' class='answer   answerof-458788 ' value='1773531'   \/><label for='answer-id-1773531' id='answer-label-1773531' class=' answer'><span>Choose the latest version in the GlueVersion field in the Glue job properties.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-34' style=';'><div id='questionWrap-34'  class='   watupro-question-id-458789'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='458789' \/><input type='hidden' id='answerType458789' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458789[]' id='answer-id-1773532' class='answer   answerof-458789 ' value='1773532'   \/><label for='answer-id-1773532' id='answer-label-1773532' class=' answer'><span>Establish WebSocket connections to Amazon Redshift.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458789[]' id='answer-id-1773533' class='answer   answerof-458789 ' value='1773533'   \/><label for='answer-id-1773533' id='answer-label-1773533' class=' answer'><span>Use the Amazon Redshift Data AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458789[]' id='answer-id-1773534' class='answer   answerof-458789 ' value='1773534'   \/><label for='answer-id-1773534' id='answer-label-1773534' class=' answer'><span>Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458789[]' id='answer-id-1773535' class='answer   answerof-458789 ' value='1773535'   \/><label for='answer-id-1773535' id='answer-label-1773535' class=' answer'><span>Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-35' style=';'><div id='questionWrap-35'  class='   watupro-question-id-458790'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size. <br \/>\r<br>A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file. <br \/>\r<br>Which solution will meet this requirement with the LEAST operational effort?<\/div><input type='hidden' name='question_id[]' id='qID_35' value='458790' \/><input type='hidden' id='answerType458790' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458790[]' id='answer-id-1773536' class='answer   answerof-458790 ' value='1773536'   \/><label for='answer-id-1773536' id='answer-label-1773536' class=' answer'><span>Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458790[]' id='answer-id-1773537' class='answer   answerof-458790 ' value='1773537'   \/><label for='answer-id-1773537' id='answer-label-1773537' class=' answer'><span>Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458790[]' id='answer-id-1773538' class='answer   answerof-458790 ' value='1773538'   \/><label for='answer-id-1773538' id='answer-label-1773538' class=' answer'><span>Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458790[]' id='answer-id-1773539' class='answer   answerof-458790 ' value='1773539'   \/><label for='answer-id-1773539' id='answer-label-1773539' class=' answer'><span>Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-36' style=';'><div id='questionWrap-36'  class='   watupro-question-id-458791'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational effort?<\/div><input type='hidden' name='question_id[]' id='qID_36' value='458791' \/><input type='hidden' id='answerType458791' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458791[]' id='answer-id-1773540' class='answer   answerof-458791 ' value='1773540'   \/><label for='answer-id-1773540' id='answer-label-1773540' class=' answer'><span>Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458791[]' id='answer-id-1773541' class='answer   answerof-458791 ' value='1773541'   \/><label for='answer-id-1773541' id='answer-label-1773541' class=' answer'><span>Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458791[]' id='answer-id-1773542' class='answer   answerof-458791 ' value='1773542'   \/><label for='answer-id-1773542' id='answer-label-1773542' class=' answer'><span>Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458791[]' id='answer-id-1773543' class='answer   answerof-458791 ' value='1773543'   \/><label for='answer-id-1773543' id='answer-label-1773543' class=' answer'><span>Load the data into Amazon Redshift. Create a view for each country. Create separate 1AM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-37' style=';'><div id='questionWrap-37'  class='   watupro-question-id-458792'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data. <br \/>\r<br>The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_37' value='458792' \/><input type='hidden' id='answerType458792' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458792[]' id='answer-id-1773544' class='answer   answerof-458792 ' value='1773544'   \/><label for='answer-id-1773544' id='answer-label-1773544' class=' answer'><span>AWS Glue workflows<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458792[]' id='answer-id-1773545' class='answer   answerof-458792 ' value='1773545'   \/><label for='answer-id-1773545' id='answer-label-1773545' class=' answer'><span>AWS Step Functions tasks<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458792[]' id='answer-id-1773546' class='answer   answerof-458792 ' value='1773546'   \/><label for='answer-id-1773546' id='answer-label-1773546' class=' answer'><span>AWS Lambda functions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458792[]' id='answer-id-1773547' class='answer   answerof-458792 ' value='1773547'   \/><label for='answer-id-1773547' id='answer-label-1773547' class=' answer'><span>Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-38' style=';'><div id='questionWrap-38'  class='   watupro-question-id-458793'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository. <br \/>\r<br>Which solution will meet these requirements with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_38' value='458793' \/><input type='hidden' id='answerType458793' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458793[]' id='answer-id-1773548' class='answer   answerof-458793 ' value='1773548'   \/><label for='answer-id-1773548' id='answer-label-1773548' class=' answer'><span>Use Amazon EMR and Apache Ranger.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458793[]' id='answer-id-1773549' class='answer   answerof-458793 ' value='1773549'   \/><label for='answer-id-1773549' id='answer-label-1773549' class=' answer'><span>Use a Hive metastore on an EMR cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458793[]' id='answer-id-1773550' class='answer   answerof-458793 ' value='1773550'   \/><label for='answer-id-1773550' id='answer-label-1773550' class=' answer'><span>Use the AWS Glue Data Catalog.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458793[]' id='answer-id-1773551' class='answer   answerof-458793 ' value='1773551'   \/><label for='answer-id-1773551' id='answer-label-1773551' class=' answer'><span>Use a metastore on an Amazon RDS for MySQL DB instance.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-39' style=';'><div id='questionWrap-39'  class='   watupro-question-id-458794'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures. <br \/>\r<br>The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date. <br \/>\r<br>As the amount of data increases, the company wants to optimize the storage solution to improve query performance. <br \/>\r<br>Which combination of solutions will meet these requirements? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_39' value='458794' \/><input type='hidden' id='answerType458794' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458794[]' id='answer-id-1773552' class='answer   answerof-458794 ' value='1773552'   \/><label for='answer-id-1773552' id='answer-label-1773552' class=' answer'><span>Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458794[]' id='answer-id-1773553' class='answer   answerof-458794 ' value='1773553'   \/><label for='answer-id-1773553' id='answer-label-1773553' class=' answer'><span>Use an S3 bucket that is in the same account that uses Athena to query the data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458794[]' id='answer-id-1773554' class='answer   answerof-458794 ' value='1773554'   \/><label for='answer-id-1773554' id='answer-label-1773554' class=' answer'><span>Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458794[]' id='answer-id-1773555' class='answer   answerof-458794 ' value='1773555'   \/><label for='answer-id-1773555' id='answer-label-1773555' class=' answer'><span>Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458794[]' id='answer-id-1773556' class='answer   answerof-458794 ' value='1773556'   \/><label for='answer-id-1773556' id='answer-label-1773556' class=' answer'><span>Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-40' style=';'><div id='questionWrap-40'  class='   watupro-question-id-458795'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time. <br \/>\r<br>The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='458795' \/><input type='hidden' id='answerType458795' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458795[]' id='answer-id-1773557' class='answer   answerof-458795 ' value='1773557'   \/><label for='answer-id-1773557' id='answer-label-1773557' class=' answer'><span>Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458795[]' id='answer-id-1773558' class='answer   answerof-458795 ' value='1773558'   \/><label for='answer-id-1773558' id='answer-label-1773558' class=' answer'><span>Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458795[]' id='answer-id-1773559' class='answer   answerof-458795 ' value='1773559'   \/><label for='answer-id-1773559' id='answer-label-1773559' class=' answer'><span>Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458795[]' id='answer-id-1773560' class='answer   answerof-458795 ' value='1773560'   \/><label for='answer-id-1773560' id='answer-label-1773560' class=' answer'><span>Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-41' style=';'><div id='questionWrap-41'  class='   watupro-question-id-458796'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>41. <\/span>A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_41' value='458796' \/><input type='hidden' id='answerType458796' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458796[]' id='answer-id-1773561' class='answer   answerof-458796 ' value='1773561'   \/><label for='answer-id-1773561' id='answer-label-1773561' class=' answer'><span>Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458796[]' id='answer-id-1773562' class='answer   answerof-458796 ' value='1773562'   \/><label for='answer-id-1773562' id='answer-label-1773562' class=' answer'><span>Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458796[]' id='answer-id-1773563' class='answer   answerof-458796 ' value='1773563'   \/><label for='answer-id-1773563' id='answer-label-1773563' class=' answer'><span>Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an \r\nAmazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458796[]' id='answer-id-1773564' class='answer   answerof-458796 ' value='1773564'   \/><label for='answer-id-1773564' id='answer-label-1773564' class=' answer'><span>Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-42' style=';'><div id='questionWrap-42'  class='   watupro-question-id-458797'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>42. <\/span>A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.<br \/>\r\n<br \/>\r\nThe data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.<br \/>\r\n<br \/>\r\nWhich solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_42' value='458797' \/><input type='hidden' id='answerType458797' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458797[]' id='answer-id-1773565' class='answer   answerof-458797 ' value='1773565'   \/><label for='answer-id-1773565' id='answer-label-1773565' class=' answer'><span>Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458797[]' id='answer-id-1851014' class='answer   answerof-458797 ' value='1851014'   \/><label for='answer-id-1851014' id='answer-label-1851014' class=' answer'><span>Use Amazon Redshift Serverless to automatically process the analytics workload.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458797[]' id='answer-id-1851015' class='answer   answerof-458797 ' value='1851015'   \/><label for='answer-id-1851015' id='answer-label-1851015' class=' answer'><span>Use the AWS CLI to automatically process the analytics workload.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458797[]' id='answer-id-1851016' class='answer   answerof-458797 ' value='1851016'   \/><label for='answer-id-1851016' id='answer-label-1851016' class=' answer'><span>Use AWS CloudFormation templates to automatically process the analytics workload.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-43' style=';'><div id='questionWrap-43'  class='   watupro-question-id-458798'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>43. <\/span>A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_43' value='458798' \/><input type='hidden' id='answerType458798' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458798[]' id='answer-id-1773566' class='answer   answerof-458798 ' value='1773566'   \/><label for='answer-id-1773566' id='answer-label-1773566' class=' answer'><span>Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458798[]' id='answer-id-1773567' class='answer   answerof-458798 ' value='1773567'   \/><label for='answer-id-1773567' id='answer-label-1773567' class=' answer'><span>Create an AWS Lambda Python function with provisioned concurrency.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458798[]' id='answer-id-1773568' class='answer   answerof-458798 ' value='1773568'   \/><label for='answer-id-1773568' id='answer-label-1773568' class=' answer'><span>Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458798[]' id='answer-id-1773569' class='answer   answerof-458798 ' value='1773569'   \/><label for='answer-id-1773569' id='answer-label-1773569' class=' answer'><span>Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon \r\nEventBridge rule to invoke the Lambda function every 5 minutes by using mock events.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-44' style=';'><div id='questionWrap-44'  class='   watupro-question-id-458799'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>44. <\/span>A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require. <br \/>\r<br>Which solution will meet these requirements with the LEAST effort?<\/div><input type='hidden' name='question_id[]' id='qID_44' value='458799' \/><input type='hidden' id='answerType458799' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458799[]' id='answer-id-1773570' class='answer   answerof-458799 ' value='1773570'   \/><label for='answer-id-1773570' id='answer-label-1773570' class=' answer'><span>Use Amazon Athena to query the data. Set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. Assign each user to the IAM role that matches \r\nthe user's PII access requirements.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458799[]' id='answer-id-1773571' class='answer   answerof-458799 ' value='1773571'   \/><label for='answer-id-1773571' id='answer-label-1773571' class=' answer'><span>Use Amazon QuickSight to access the data. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athena. Define QuickSight access levels based on the PII access requirements of the users.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458799[]' id='answer-id-1773572' class='answer   answerof-458799 ' value='1773572'   \/><label for='answer-id-1773572' id='answer-label-1773572' class=' answer'><span>Build a custom query builder UI that will run Athena queries in the background to access the data. Create user groups in Amazon Cognito. Assign access levels to the user groups based on the PII access requirements of the users.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458799[]' id='answer-id-1773573' class='answer   answerof-458799 ' value='1773573'   \/><label for='answer-id-1773573' id='answer-label-1773573' class=' answer'><span>Create IAM roles that have different levels of granular access. Assign the IAM roles to IAM user groups. Use an identity-based policy to assign access levels to user groups at the column level.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-45' style=';'><div id='questionWrap-45'  class='   watupro-question-id-458800'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>45. <\/span>A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. <br \/>\r<br>A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. <br \/>\r<br>Which solution will capture the changed data MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_45' value='458800' \/><input type='hidden' id='answerType458800' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458800[]' id='answer-id-1773574' class='answer   answerof-458800 ' value='1773574'   \/><label for='answer-id-1773574' id='answer-label-1773574' class=' answer'><span>Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458800[]' id='answer-id-1773575' class='answer   answerof-458800 ' value='1773575'   \/><label for='answer-id-1773575' id='answer-label-1773575' class=' answer'><span>Ingest the data into Amazon RDS for MySQ<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458800[]' id='answer-id-1773576' class='answer   answerof-458800 ' value='1773576'   \/><label for='answer-id-1773576' id='answer-label-1773576' class=' answer'><span>Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458800[]' id='answer-id-1773577' class='answer   answerof-458800 ' value='1773577'   \/><label for='answer-id-1773577' id='answer-label-1773577' class=' answer'><span>Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458800[]' id='answer-id-1773578' class='answer   answerof-458800 ' value='1773578'   \/><label for='answer-id-1773578' id='answer-label-1773578' class=' answer'><span>Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-46' style=';'><div id='questionWrap-46'  class='   watupro-question-id-458801'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>46. <\/span>A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations. <br \/>\r<br>Which combination of AWS services will implement a data mesh? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_46' value='458801' \/><input type='hidden' id='answerType458801' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458801[]' id='answer-id-1773579' class='answer   answerof-458801 ' value='1773579'   \/><label for='answer-id-1773579' id='answer-label-1773579' class=' answer'><span>Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458801[]' id='answer-id-1773580' class='answer   answerof-458801 ' value='1773580'   \/><label for='answer-id-1773580' id='answer-label-1773580' class=' answer'><span>Use Amazon S3 for data storage. Use Amazon Athena for data analysis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458801[]' id='answer-id-1773581' class='answer   answerof-458801 ' value='1773581'   \/><label for='answer-id-1773581' id='answer-label-1773581' class=' answer'><span>Use AWS Glue DataBrewfor centralized data governance and access control.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458801[]' id='answer-id-1773582' class='answer   answerof-458801 ' value='1773582'   \/><label for='answer-id-1773582' id='answer-label-1773582' class=' answer'><span>Use Amazon RDS for data storage. Use Amazon EMR for data analysis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458801[]' id='answer-id-1773583' class='answer   answerof-458801 ' value='1773583'   \/><label for='answer-id-1773583' id='answer-label-1773583' class=' answer'><span>Use AWS Lake Formation for centralized data governance and access control.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-47' style=';'><div id='questionWrap-47'  class='   watupro-question-id-458802'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>47. <\/span>A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions. <br \/>\r<br>Which factors could cause to the permissions-related errors? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_47' value='458802' \/><input type='hidden' id='answerType458802' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458802[]' id='answer-id-1773584' class='answer   answerof-458802 ' value='1773584'   \/><label for='answer-id-1773584' id='answer-label-1773584' class=' answer'><span>There is no connection between QuickSgqht and Athena.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458802[]' id='answer-id-1773585' class='answer   answerof-458802 ' value='1773585'   \/><label for='answer-id-1773585' id='answer-label-1773585' class=' answer'><span>The Athena tables are not cataloged.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458802[]' id='answer-id-1773586' class='answer   answerof-458802 ' value='1773586'   \/><label for='answer-id-1773586' id='answer-label-1773586' class=' answer'><span>QuickSiqht does not have access to the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458802[]' id='answer-id-1773587' class='answer   answerof-458802 ' value='1773587'   \/><label for='answer-id-1773587' id='answer-label-1773587' class=' answer'><span>QuickSight does not have access to decrypt S3 data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458802[]' id='answer-id-1773588' class='answer   answerof-458802 ' value='1773588'   \/><label for='answer-id-1773588' id='answer-label-1773588' class=' answer'><span>There is no 1AM role assigned to QuickSiqht.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-48' style=';'><div id='questionWrap-48'  class='   watupro-question-id-458803'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>48. <\/span>A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks. <br \/>\r<br>The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster. <br \/>\r<br>The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_48' value='458803' \/><input type='hidden' id='answerType458803' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458803[]' id='answer-id-1773589' class='answer   answerof-458803 ' value='1773589'   \/><label for='answer-id-1773589' id='answer-label-1773589' class=' answer'><span>Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458803[]' id='answer-id-1773590' class='answer   answerof-458803 ' value='1773590'   \/><label for='answer-id-1773590' id='answer-label-1773590' class=' answer'><span>Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458803[]' id='answer-id-1773591' class='answer   answerof-458803 ' value='1773591'   \/><label for='answer-id-1773591' id='answer-label-1773591' class=' answer'><span>Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458803[]' id='answer-id-1773592' class='answer   answerof-458803 ' value='1773592'   \/><label for='answer-id-1773592' id='answer-label-1773592' class=' answer'><span>Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-49' style=';'><div id='questionWrap-49'  class='   watupro-question-id-458804'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>49. <\/span>A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services. <br \/>\r<br>Which solution will meet these requirements with the LEAST management overhead?<\/div><input type='hidden' name='question_id[]' id='qID_49' value='458804' \/><input type='hidden' id='answerType458804' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458804[]' id='answer-id-1773593' class='answer   answerof-458804 ' value='1773593'   \/><label for='answer-id-1773593' id='answer-label-1773593' class=' answer'><span>Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458804[]' id='answer-id-1773594' class='answer   answerof-458804 ' value='1773594'   \/><label for='answer-id-1773594' id='answer-label-1773594' class=' answer'><span>Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458804[]' id='answer-id-1773595' class='answer   answerof-458804 ' value='1773595'   \/><label for='answer-id-1773595' id='answer-label-1773595' class=' answer'><span>Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458804[]' id='answer-id-1773596' class='answer   answerof-458804 ' value='1773596'   \/><label for='answer-id-1773596' id='answer-label-1773596' class=' answer'><span>Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and \r\nthe second task is to call the AWS Glue job.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-50' style=';'><div id='questionWrap-50'  class='   watupro-question-id-458805'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>50. <\/span>A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data. <br \/>\r<br>Which solution will meet these requirements MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_50' value='458805' \/><input type='hidden' id='answerType458805' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458805[]' id='answer-id-1773597' class='answer   answerof-458805 ' value='1773597'   \/><label for='answer-id-1773597' id='answer-label-1773597' class=' answer'><span>Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458805[]' id='answer-id-1773598' class='answer   answerof-458805 ' value='1773598'   \/><label for='answer-id-1773598' id='answer-label-1773598' class=' answer'><span>Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458805[]' id='answer-id-1773599' class='answer   answerof-458805 ' value='1773599'   \/><label for='answer-id-1773599' id='answer-label-1773599' class=' answer'><span>Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458805[]' id='answer-id-1773600' class='answer   answerof-458805 ' value='1773600'   \/><label for='answer-id-1773600' id='answer-label-1773600' class=' answer'><span>Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458805[]' id='answer-id-1773601' class='answer   answerof-458805 ' value='1773601'   \/><label for='answer-id-1773601' id='answer-label-1773601' class=' answer'><span>Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-51'>\n\t<div class='question-content'>\n\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading...\" title=\"Loading...\" \/>&nbsp;Loading...\t<\/div>\n<\/div>\n\n<br \/>\n\t\n\t\t\t<div class=\"watupro_buttons flex \" id=\"watuPROButtons11693\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; 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We provide a streamlined and effective preparation experience with valid exam questions and answers, ensuring your success in the AWS Certified Data Engineer &#8211; Associate (DEA-C01) exam. All the 231 practice questions and answers in the V12.02 are curated by subject matter experts to ensure [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[175,18249],"tags":[20211],"class_list":["post-125764","post","type-post","status-publish","format-standard","hentry","category-amazon","category-data-engineer-associate","tag-amazon-dea-c01"],"_links":{"self":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/125764","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/comments?post=125764"}],"version-history":[{"count":2,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/125764\/revisions"}],"predecessor-version":[{"id":125766,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/125764\/revisions\/125766"}],"wp:attachment":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/media?parent=125764"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/categories?post=125764"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/tags?post=125764"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}