{"id":115893,"date":"2025-12-11T07:15:58","date_gmt":"2025-12-11T07:15:58","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=115893"},"modified":"2025-12-11T07:15:58","modified_gmt":"2025-12-11T07:15:58","slug":"aws-dea-c01-dumps-v11-02-help-you-pass-the-aws-certified-data-engineer-associate-exam-dea-c01-free-dumps-part-2-q41-q65-are-available","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/aws-dea-c01-dumps-v11-02-help-you-pass-the-aws-certified-data-engineer-associate-exam-dea-c01-free-dumps-part-2-q41-q65-are-available.html","title":{"rendered":"AWS DEA-C01 Dumps (V11.02) Help You Pass the AWS Certified Data Engineer &#8211; Associate Exam: DEA-C01 Free Dumps (Part 2, Q41-Q65) Are Available"},"content":{"rendered":"<p>To help you pass the AWS Certified Data Engineer &#8211; Associate (DEA-C01) exam, you must understand what exam questions might be asked in the actual exam. Then you can choose the AWS DEA-C01 dumps (V11.02) to start your preparation. DumpsBase provides a complete dump with real questions and answers, ensuring your success on the first attempt. We have shared the <a href=\"https:\/\/www.dumpsbase.com\/freedumps\/pass-the-aws-certified-data-engineer-associate-dea-c01-exam-by-using-the-dea-c01-dumps-v11-02-read-dea-c01-free-dumps-part-1-q1-q40-first.html\"><em><strong>DEA-C01 free dumps (Part 1, Q1-Q40) of V11.02<\/strong><\/em><\/a> to help you check the quality. From these demos, you must believe that DumpsBase always tries its best to improve your Amazon DEA-C01 exam preparation. Today, we will continue to share more free demos online to help you check more about the AWS DEA-C01 dumps (V11.02).<\/p>\n<h2>Continue to check the <span style=\"background-color: #ffcc99;\"><em>DEA-C01 free dumps (Part 2, Q41-Q65) of V11.02 below<\/em><\/span>:<\/h2>\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=\"submittingExam11029\" 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-11029\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11029\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-434300'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/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_1' value='434300' \/><input type='hidden' id='answerType434300' 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-434300[]' id='answer-id-1680494' class='answer   answerof-434300 ' value='1680494'   \/><label for='answer-id-1680494' id='answer-label-1680494' 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-434300[]' id='answer-id-1680495' class='answer   answerof-434300 ' value='1680495'   \/><label for='answer-id-1680495' id='answer-label-1680495' 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-434300[]' id='answer-id-1680496' class='answer   answerof-434300 ' value='1680496'   \/><label for='answer-id-1680496' id='answer-label-1680496' 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-434300[]' id='answer-id-1680497' class='answer   answerof-434300 ' value='1680497'   \/><label for='answer-id-1680497' id='answer-label-1680497' 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-434300[]' id='answer-id-1680498' class='answer   answerof-434300 ' value='1680498'   \/><label for='answer-id-1680498' id='answer-label-1680498' 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-2' style=';'><div id='questionWrap-2'  class='   watupro-question-id-434301'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. <br \/>\r<br>Which solution will meet this requirement MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='434301' \/><input type='hidden' id='answerType434301' 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-434301[]' id='answer-id-1680499' class='answer   answerof-434301 ' value='1680499'   \/><label for='answer-id-1680499' id='answer-label-1680499' class=' answer'><span>Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434301[]' id='answer-id-1680500' class='answer   answerof-434301 ' value='1680500'   \/><label for='answer-id-1680500' id='answer-label-1680500' class=' answer'><span>Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434301[]' id='answer-id-1680501' class='answer   answerof-434301 ' value='1680501'   \/><label for='answer-id-1680501' id='answer-label-1680501' class=' answer'><span>Use Amazon Athena Federated Query to join the data from all data sources.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434301[]' id='answer-id-1680502' class='answer   answerof-434301 ' value='1680502'   \/><label for='answer-id-1680502' id='answer-label-1680502' class=' answer'><span>Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.<\/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-434302'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='434302' \/><input type='hidden' id='answerType434302' 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-434302[]' id='answer-id-1680503' class='answer   answerof-434302 ' value='1680503'   \/><label for='answer-id-1680503' id='answer-label-1680503' class=' answer'><span>Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434302[]' id='answer-id-1680504' class='answer   answerof-434302 ' value='1680504'   \/><label for='answer-id-1680504' id='answer-label-1680504' class=' answer'><span>Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434302[]' id='answer-id-1680505' class='answer   answerof-434302 ' value='1680505'   \/><label for='answer-id-1680505' id='answer-label-1680505' class=' answer'><span>Create an JAM role for each use case. Assign appropriate permissions to the role for each use case. \r\nAssociate the role with Athena.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434302[]' id='answer-id-1680506' class='answer   answerof-434302 ' value='1680506'   \/><label for='answer-id-1680506' id='answer-label-1680506' class=' answer'><span>Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.<\/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-434303'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/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_4' value='434303' \/><input type='hidden' id='answerType434303' 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-434303[]' id='answer-id-1680507' class='answer   answerof-434303 ' value='1680507'   \/><label for='answer-id-1680507' id='answer-label-1680507' 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-434303[]' id='answer-id-1680508' class='answer   answerof-434303 ' value='1680508'   \/><label for='answer-id-1680508' id='answer-label-1680508' 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-434303[]' id='answer-id-1680509' class='answer   answerof-434303 ' value='1680509'   \/><label for='answer-id-1680509' id='answer-label-1680509' 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-434303[]' id='answer-id-1680510' class='answer   answerof-434303 ' value='1680510'   \/><label for='answer-id-1680510' id='answer-label-1680510' 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-5' style=';'><div id='questionWrap-5'  class='   watupro-question-id-434304'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/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_5' value='434304' \/><input type='hidden' id='answerType434304' 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-434304[]' id='answer-id-1680511' class='answer   answerof-434304 ' value='1680511'   \/><label for='answer-id-1680511' id='answer-label-1680511' 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-434304[]' id='answer-id-1680512' class='answer   answerof-434304 ' value='1680512'   \/><label for='answer-id-1680512' id='answer-label-1680512' 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-434304[]' id='answer-id-1680513' class='answer   answerof-434304 ' value='1680513'   \/><label for='answer-id-1680513' id='answer-label-1680513' 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-434304[]' id='answer-id-1680514' class='answer   answerof-434304 ' value='1680514'   \/><label for='answer-id-1680514' id='answer-label-1680514' 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-434304[]' id='answer-id-1680515' class='answer   answerof-434304 ' value='1680515'   \/><label for='answer-id-1680515' id='answer-label-1680515' 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-6' style=';'><div id='questionWrap-6'  class='   watupro-question-id-434305'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/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_6' value='434305' \/><input type='hidden' id='answerType434305' 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-434305[]' id='answer-id-1680516' class='answer   answerof-434305 ' value='1680516'   \/><label for='answer-id-1680516' id='answer-label-1680516' 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-434305[]' id='answer-id-1680517' class='answer   answerof-434305 ' value='1680517'   \/><label for='answer-id-1680517' id='answer-label-1680517' 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-434305[]' id='answer-id-1680518' class='answer   answerof-434305 ' value='1680518'   \/><label for='answer-id-1680518' id='answer-label-1680518' 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-434305[]' id='answer-id-1680519' class='answer   answerof-434305 ' value='1680519'   \/><label for='answer-id-1680519' id='answer-label-1680519' 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-434305[]' id='answer-id-1680520' class='answer   answerof-434305 ' value='1680520'   \/><label for='answer-id-1680520' id='answer-label-1680520' 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-7' style=';'><div id='questionWrap-7'  class='   watupro-question-id-434306'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/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_7' value='434306' \/><input type='hidden' id='answerType434306' 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-434306[]' id='answer-id-1680521' class='answer   answerof-434306 ' value='1680521'   \/><label for='answer-id-1680521' id='answer-label-1680521' 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-434306[]' id='answer-id-1680522' class='answer   answerof-434306 ' value='1680522'   \/><label for='answer-id-1680522' id='answer-label-1680522' 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-434306[]' id='answer-id-1680523' class='answer   answerof-434306 ' value='1680523'   \/><label for='answer-id-1680523' id='answer-label-1680523' 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-434306[]' id='answer-id-1680524' class='answer   answerof-434306 ' value='1680524'   \/><label for='answer-id-1680524' id='answer-label-1680524' 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 the 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-8' style=';'><div id='questionWrap-8'  class='   watupro-question-id-434307'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/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_8' value='434307' \/><input type='hidden' id='answerType434307' 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-434307[]' id='answer-id-1680525' class='answer   answerof-434307 ' value='1680525'   \/><label for='answer-id-1680525' id='answer-label-1680525' 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-434307[]' id='answer-id-1680526' class='answer   answerof-434307 ' value='1680526'   \/><label for='answer-id-1680526' id='answer-label-1680526' 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-434307[]' id='answer-id-1680527' class='answer   answerof-434307 ' value='1680527'   \/><label for='answer-id-1680527' id='answer-label-1680527' 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-434307[]' id='answer-id-1680528' class='answer   answerof-434307 ' value='1680528'   \/><label for='answer-id-1680528' id='answer-label-1680528' 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-9' style=';'><div id='questionWrap-9'  class='   watupro-question-id-434308'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/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_9' value='434308' \/><input type='hidden' id='answerType434308' 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-434308[]' id='answer-id-1680529' class='answer   answerof-434308 ' value='1680529'   \/><label for='answer-id-1680529' id='answer-label-1680529' class=' answer'><span>AWS Glue workflows<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434308[]' id='answer-id-1680530' class='answer   answerof-434308 ' value='1680530'   \/><label for='answer-id-1680530' id='answer-label-1680530' class=' answer'><span>AWS Step Functions tasks<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434308[]' id='answer-id-1680531' class='answer   answerof-434308 ' value='1680531'   \/><label for='answer-id-1680531' id='answer-label-1680531' class=' answer'><span>AWS Lambda functions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434308[]' id='answer-id-1680532' class='answer   answerof-434308 ' value='1680532'   \/><label for='answer-id-1680532' id='answer-label-1680532' 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-10' style=';'><div id='questionWrap-10'  class='   watupro-question-id-434309'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/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_10' value='434309' \/><input type='hidden' id='answerType434309' 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-434309[]' id='answer-id-1680533' class='answer   answerof-434309 ' value='1680533'   \/><label for='answer-id-1680533' id='answer-label-1680533' 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-434309[]' id='answer-id-1680534' class='answer   answerof-434309 ' value='1680534'   \/><label for='answer-id-1680534' id='answer-label-1680534' 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-434309[]' id='answer-id-1680535' class='answer   answerof-434309 ' value='1680535'   \/><label for='answer-id-1680535' id='answer-label-1680535' 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-434309[]' id='answer-id-1680536' class='answer   answerof-434309 ' value='1680536'   \/><label for='answer-id-1680536' id='answer-label-1680536' 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-434309[]' id='answer-id-1680537' class='answer   answerof-434309 ' value='1680537'   \/><label for='answer-id-1680537' id='answer-label-1680537' 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-11' style=';'><div id='questionWrap-11'  class='   watupro-question-id-434310'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift. <br \/>\r<br>The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs. <br \/>\r<br>Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_11' value='434310' \/><input type='hidden' id='answerType434310' 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-434310[]' id='answer-id-1680538' class='answer   answerof-434310 ' value='1680538'   \/><label for='answer-id-1680538' id='answer-label-1680538' class=' answer'><span>Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434310[]' id='answer-id-1680539' class='answer   answerof-434310 ' value='1680539'   \/><label for='answer-id-1680539' id='answer-label-1680539' class=' answer'><span>Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434310[]' id='answer-id-1680540' class='answer   answerof-434310 ' value='1680540'   \/><label for='answer-id-1680540' id='answer-label-1680540' class=' answer'><span>Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EK<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434310[]' id='answer-id-1680541' class='answer   answerof-434310 ' value='1680541'   \/><label for='answer-id-1680541' id='answer-label-1680541' class=' answer'><span>Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434310[]' id='answer-id-1680542' class='answer   answerof-434310 ' value='1680542'   \/><label for='answer-id-1680542' id='answer-label-1680542' class=' answer'><span>Query the flow logs for the VP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434310[]' id='answer-id-1680543' class='answer   answerof-434310 ' value='1680543'   \/><label for='answer-id-1680543' id='answer-label-1680543' class=' answer'><span>Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434310[]' id='answer-id-1680544' class='answer   answerof-434310 ' value='1680544'   \/><label for='answer-id-1680544' id='answer-label-1680544' class=' answer'><span>Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.<\/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-434311'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket. <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='434311' \/><input type='hidden' id='answerType434311' 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-434311[]' id='answer-id-1680545' class='answer   answerof-434311 ' value='1680545'   \/><label for='answer-id-1680545' id='answer-label-1680545' class=' answer'><span>Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434311[]' id='answer-id-1680546' class='answer   answerof-434311 ' value='1680546'   \/><label for='answer-id-1680546' id='answer-label-1680546' class=' answer'><span>Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434311[]' id='answer-id-1680547' class='answer   answerof-434311 ' value='1680547'   \/><label for='answer-id-1680547' id='answer-label-1680547' class=' answer'><span>Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434311[]' id='answer-id-1680548' class='answer   answerof-434311 ' value='1680548'   \/><label for='answer-id-1680548' id='answer-label-1680548' class=' answer'><span>Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.<\/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-434312'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options. <br \/>\r<br>The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS. <br \/>\r<br>Which extract, transform, and load (ETL) service will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='434312' \/><input type='hidden' id='answerType434312' 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-434312[]' id='answer-id-1680549' class='answer   answerof-434312 ' value='1680549'   \/><label for='answer-id-1680549' id='answer-label-1680549' class=' answer'><span>AWS Glue<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434312[]' id='answer-id-1680550' class='answer   answerof-434312 ' value='1680550'   \/><label for='answer-id-1680550' id='answer-label-1680550' class=' answer'><span>Amazon EMR<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434312[]' id='answer-id-1680551' class='answer   answerof-434312 ' value='1680551'   \/><label for='answer-id-1680551' id='answer-label-1680551' class=' answer'><span>AWS Lambda<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434312[]' id='answer-id-1680552' class='answer   answerof-434312 ' value='1680552'   \/><label for='answer-id-1680552' id='answer-label-1680552' class=' answer'><span>Amazon Redshift<\/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-434313'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file. <br \/>\r<br>Which Step Functions state should the data engineer use to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='434313' \/><input type='hidden' id='answerType434313' 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-434313[]' id='answer-id-1680553' class='answer   answerof-434313 ' value='1680553'   \/><label for='answer-id-1680553' id='answer-label-1680553' class=' answer'><span>Parallel state<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434313[]' id='answer-id-1680554' class='answer   answerof-434313 ' value='1680554'   \/><label for='answer-id-1680554' id='answer-label-1680554' class=' answer'><span>Choice state<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434313[]' id='answer-id-1680555' class='answer   answerof-434313 ' value='1680555'   \/><label for='answer-id-1680555' id='answer-label-1680555' class=' answer'><span>Map state<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434313[]' id='answer-id-1680556' class='answer   answerof-434313 ' value='1680556'   \/><label for='answer-id-1680556' id='answer-label-1680556' class=' answer'><span>Wait state<\/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-434314'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='434314' \/><input type='hidden' id='answerType434314' 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-434314[]' id='answer-id-1680557' class='answer   answerof-434314 ' value='1680557'   \/><label for='answer-id-1680557' id='answer-label-1680557' class=' answer'><span>Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434314[]' id='answer-id-1680558' class='answer   answerof-434314 ' value='1680558'   \/><label for='answer-id-1680558' id='answer-label-1680558' class=' answer'><span>Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434314[]' id='answer-id-1680559' class='answer   answerof-434314 ' value='1680559'   \/><label for='answer-id-1680559' id='answer-label-1680559' class=' answer'><span>Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434314[]' id='answer-id-1680560' class='answer   answerof-434314 ' value='1680560'   \/><label for='answer-id-1680560' id='answer-label-1680560' class=' answer'><span>Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.<\/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-434315'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file. <br \/>\r<br>Which solution will meet these requirements MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='434315' \/><input type='hidden' id='answerType434315' 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-434315[]' id='answer-id-1680561' class='answer   answerof-434315 ' value='1680561'   \/><label for='answer-id-1680561' id='answer-label-1680561' class=' answer'><span>Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434315[]' id='answer-id-1680562' class='answer   answerof-434315 ' value='1680562'   \/><label for='answer-id-1680562' id='answer-label-1680562' class=' answer'><span>Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434315[]' id='answer-id-1680563' class='answer   answerof-434315 ' value='1680563'   \/><label for='answer-id-1680563' id='answer-label-1680563' class=' answer'><span>Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434315[]' id='answer-id-1680564' class='answer   answerof-434315 ' value='1680564'   \/><label for='answer-id-1680564' id='answer-label-1680564' class=' answer'><span>Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.<\/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-434316'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/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_17' value='434316' \/><input type='hidden' id='answerType434316' 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-434316[]' id='answer-id-1680565' class='answer   answerof-434316 ' value='1680565'   \/><label for='answer-id-1680565' id='answer-label-1680565' 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-434316[]' id='answer-id-1680566' class='answer   answerof-434316 ' value='1680566'   \/><label for='answer-id-1680566' id='answer-label-1680566' 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-434316[]' id='answer-id-1680567' class='answer   answerof-434316 ' value='1680567'   \/><label for='answer-id-1680567' id='answer-label-1680567' 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-434316[]' id='answer-id-1680568' class='answer   answerof-434316 ' value='1680568'   \/><label for='answer-id-1680568' id='answer-label-1680568' 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-434316[]' id='answer-id-1680569' class='answer   answerof-434316 ' value='1680569'   \/><label for='answer-id-1680569' id='answer-label-1680569' 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-18' style=';'><div id='questionWrap-18'  class='   watupro-question-id-434317'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/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_18' value='434317' \/><input type='hidden' id='answerType434317' 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-434317[]' id='answer-id-1680570' class='answer   answerof-434317 ' value='1680570'   \/><label for='answer-id-1680570' id='answer-label-1680570' 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-434317[]' id='answer-id-1680571' class='answer   answerof-434317 ' value='1680571'   \/><label for='answer-id-1680571' id='answer-label-1680571' 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-434317[]' id='answer-id-1680572' class='answer   answerof-434317 ' value='1680572'   \/><label for='answer-id-1680572' id='answer-label-1680572' 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-434317[]' id='answer-id-1680573' class='answer   answerof-434317 ' value='1680573'   \/><label for='answer-id-1680573' id='answer-label-1680573' 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-434317[]' id='answer-id-1680574' class='answer   answerof-434317 ' value='1680574'   \/><label for='answer-id-1680574' id='answer-label-1680574' 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 class='watu-question ' id='question-19' style=';'><div id='questionWrap-19'  class='   watupro-question-id-434318'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/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_19' value='434318' \/><input type='hidden' id='answerType434318' 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-434318[]' id='answer-id-1680575' class='answer   answerof-434318 ' value='1680575'   \/><label for='answer-id-1680575' id='answer-label-1680575' class=' answer'><span>AWS Step Functions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434318[]' id='answer-id-1680576' class='answer   answerof-434318 ' value='1680576'   \/><label for='answer-id-1680576' id='answer-label-1680576' class=' answer'><span>AWS Glue workflows<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434318[]' id='answer-id-1680577' class='answer   answerof-434318 ' value='1680577'   \/><label for='answer-id-1680577' id='answer-label-1680577' class=' answer'><span>AWS Glue Studio<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434318[]' id='answer-id-1680578' class='answer   answerof-434318 ' value='1680578'   \/><label for='answer-id-1680578' id='answer-label-1680578' 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-20' style=';'><div id='questionWrap-20'  class='   watupro-question-id-434319'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/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_20' value='434319' \/><input type='hidden' id='answerType434319' 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-434319[]' id='answer-id-1680579' class='answer   answerof-434319 ' value='1680579'   \/><label for='answer-id-1680579' id='answer-label-1680579' 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-434319[]' id='answer-id-1680580' class='answer   answerof-434319 ' value='1680580'   \/><label for='answer-id-1680580' id='answer-label-1680580' 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-434319[]' id='answer-id-1680581' class='answer   answerof-434319 ' value='1680581'   \/><label for='answer-id-1680581' id='answer-label-1680581' 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-434319[]' id='answer-id-1680582' class='answer   answerof-434319 ' value='1680582'   \/><label for='answer-id-1680582' id='answer-label-1680582' 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-21' style=';'><div id='questionWrap-21'  class='   watupro-question-id-434320'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/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_21' value='434320' \/><input type='hidden' id='answerType434320' 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-434320[]' id='answer-id-1680583' class='answer   answerof-434320 ' value='1680583'   \/><label for='answer-id-1680583' id='answer-label-1680583' 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-434320[]' id='answer-id-1719960' class='answer   answerof-434320 ' value='1719960'   \/><label for='answer-id-1719960' id='answer-label-1719960' 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-434320[]' id='answer-id-1719961' class='answer   answerof-434320 ' value='1719961'   \/><label for='answer-id-1719961' id='answer-label-1719961' 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-434320[]' id='answer-id-1719962' class='answer   answerof-434320 ' value='1719962'   \/><label for='answer-id-1719962' id='answer-label-1719962' 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-22' style=';'><div id='questionWrap-22'  class='   watupro-question-id-434321'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/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_22' value='434321' \/><input type='hidden' id='answerType434321' 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-434321[]' id='answer-id-1680584' class='answer   answerof-434321 ' value='1680584'   \/><label for='answer-id-1680584' id='answer-label-1680584' 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-434321[]' id='answer-id-1719956' class='answer   answerof-434321 ' value='1719956'   \/><label for='answer-id-1719956' id='answer-label-1719956' 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-434321[]' id='answer-id-1719957' class='answer   answerof-434321 ' value='1719957'   \/><label for='answer-id-1719957' id='answer-label-1719957' 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-434321[]' id='answer-id-1719958' class='answer   answerof-434321 ' value='1719958'   \/><label for='answer-id-1719958' id='answer-label-1719958' 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-434321[]' id='answer-id-1719959' class='answer   answerof-434321 ' value='1719959'   \/><label for='answer-id-1719959' id='answer-label-1719959' 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-23' style=';'><div id='questionWrap-23'  class='   watupro-question-id-434322'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3. <br \/>\r<br>Which solution will meet these requirements in the MOST operationally efficient way?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='434322' \/><input type='hidden' id='answerType434322' 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-434322[]' id='answer-id-1680585' class='answer   answerof-434322 ' value='1680585'   \/><label for='answer-id-1680585' id='answer-label-1680585' class=' answer'><span>Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434322[]' id='answer-id-1680586' class='answer   answerof-434322 ' value='1680586'   \/><label for='answer-id-1680586' id='answer-label-1680586' class=' answer'><span>Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434322[]' id='answer-id-1680587' class='answer   answerof-434322 ' value='1680587'   \/><label for='answer-id-1680587' id='answer-label-1680587' class=' answer'><span>Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434322[]' id='answer-id-1680588' class='answer   answerof-434322 ' value='1680588'   \/><label for='answer-id-1680588' id='answer-label-1680588' class=' answer'><span>Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.<\/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-434323'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3. <br \/>\r<br>Which actions will provide the FASTEST queries? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_24' value='434323' \/><input type='hidden' id='answerType434323' 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-434323[]' id='answer-id-1680589' class='answer   answerof-434323 ' value='1680589'   \/><label for='answer-id-1680589' id='answer-label-1680589' class=' answer'><span>Use gzip compression to compress individual files to sizes that are between 1 GB and 5 G<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434323[]' id='answer-id-1680590' class='answer   answerof-434323 ' value='1680590'   \/><label for='answer-id-1680590' id='answer-label-1680590' class=' answer'><span>Use a columnar storage file format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434323[]' id='answer-id-1680591' class='answer   answerof-434323 ' value='1680591'   \/><label for='answer-id-1680591' id='answer-label-1680591' class=' answer'><span>Partition the data based on the most common query predicates.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434323[]' id='answer-id-1680592' class='answer   answerof-434323 ' value='1680592'   \/><label for='answer-id-1680592' id='answer-label-1680592' class=' answer'><span>Split the data into files that are less than 10 K<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434323[]' id='answer-id-1680593' class='answer   answerof-434323 ' value='1680593'   \/><label for='answer-id-1680593' id='answer-label-1680593' class=' answer'><span>Use file formats that are not<\/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-434324'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/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_25' value='434324' \/><input type='hidden' id='answerType434324' 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-434324[]' id='answer-id-1680594' class='answer   answerof-434324 ' value='1680594'   \/><label for='answer-id-1680594' id='answer-label-1680594' class=' answer'><span>Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop 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-434324[]' id='answer-id-1680595' class='answer   answerof-434324 ' value='1680595'   \/><label for='answer-id-1680595' id='answer-label-1680595' 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-434324[]' id='answer-id-1680596' class='answer   answerof-434324 ' value='1680596'   \/><label for='answer-id-1680596' id='answer-label-1680596' class=' answer'><span>Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434324[]' id='answer-id-1680597' class='answer   answerof-434324 ' value='1680597'   \/><label for='answer-id-1680597' id='answer-label-1680597' class=' answer'><span>Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. 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Then you can choose the AWS DEA-C01 dumps (V11.02) to start your preparation. DumpsBase provides a complete dump with real questions and answers, ensuring your success on the first [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[175,18249],"tags":[18538,20600],"class_list":["post-115893","post","type-post","status-publish","format-standard","hentry","category-amazon","category-data-engineer-associate","tag-aws-certified-data-engineer-associate-dea-c01","tag-aws-dea-c01-dumps"],"_links":{"self":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/115893","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=115893"}],"version-history":[{"count":1,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/115893\/revisions"}],"predecessor-version":[{"id":115894,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/115893\/revisions\/115894"}],"wp:attachment":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/media?parent=115893"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/categories?post=115893"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/tags?post=115893"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}