{"id":129070,"date":"2026-07-09T08:46:51","date_gmt":"2026-07-09T08:46:51","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=129070"},"modified":"2026-07-09T08:46:54","modified_gmt":"2026-07-09T08:46:54","slug":"aws-mla-c01-free-dumps-part-1-q1-q40-v9-02-preview-aws-certified-machine-learning-engineer-associate-updated-questions","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/aws-mla-c01-free-dumps-part-1-q1-q40-v9-02-preview-aws-certified-machine-learning-engineer-associate-updated-questions.html","title":{"rendered":"AWS MLA-C01 Free Dumps (Part 1, Q1-Q40) V9.02: Preview AWS Certified Machine Learning Engineer &#8211; Associate Updated Questions"},"content":{"rendered":"\n<p>Download the latest study materials from DumpsBase to prepare more efficiently for the AWS Certified Machine Learning Engineer \u2013 Associate (MLA-C01) exam. We have updated the dumps to V9.02, offering 200+ MLA-C01 exam questions to help you understand the latest ML Engineer exam topics. DumpsBase provides an affordable, highly accurate pathway to get familiar with the real MLA-C01 exam questions, ensuring your investment of time and money yields a passing score.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How to Practice MLA-C01 Updated Questions?<\/h2>\n\n\n\n<p>Two options to practice the MLA-C01 updated questions.<\/p>\n\n\n\n<p>MLA-C01 PDF dumps contain actual Amazon MLA-C01 exam questions arranged in an accessible, easy-to-use format.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ultimate Portability:<\/strong> Carry the file to your workplace or read questions while traveling.<\/li>\n\n\n\n<li><strong>Device Compatibility:<\/strong> Open the Amazon MLA-C01 PDF on your smartphone, laptop, or tablet without restrictions.<\/li>\n\n\n\n<li><strong>Quick Revisions:<\/strong> Revise key exam concepts whenever you have a spare moment.<\/li>\n<\/ul>\n\n\n\n<p>MLA-C01 practice test software simulates the real exam mode, being ideal for you to practice under actual test conditions.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Realistic Scenarios:<\/strong> Simulates the actual MLA-C01 exam environment and time limits.<\/li>\n\n\n\n<li><strong>Genuine Content:<\/strong> Delivers accurate Amazon MLA-C01 exam questions.<\/li>\n\n\n\n<li><strong>Performance Tracking:<\/strong> Tracks your progress and delivers quick results to identify weak areas.<\/li>\n<\/ul>\n\n\n\n<p>Leveraging the format that aligns with your lifestyle is the best way to get thoroughly familiar with the final AWS Certified Machine Learning Engineer &#8211; Associate certification exam format.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AWS MLA-C01 Free Dumps (Part 1, Q1-Q40): Preview 40 Free Sample Questions<\/h2>\n\n\n\n<p>You can preview exam questions by reading MLA-C01 free dumps. We will share 80 free demo questions in two parts, acting as a free demo of MLA-C01 dumps (V9.02). Today is the AWS MLA-C01 free dumps (Part 1, Q1-Q40), containing 40 free sample questions. They cover key AWS ML Engineer topics such as data preparation, feature engineering, SageMaker tools, model selection, evaluation, deployment, monitoring, and security. They help you review practical ML workflows across development, deployment, and maintenance, making them useful for assessing exam readiness.<\/p>\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=\"submittingExam11663\" 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-11663\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11663\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-457681'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive. <br \/>\r<br>A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database. <br \/>\r<br>Which solution will meet these requirements with the LEAST implementation effort?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='457681' \/><input type='hidden' id='answerType457681' 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-457681[]' id='answer-id-1769171' class='answer   answerof-457681 ' value='1769171'   \/><label for='answer-id-1769171' id='answer-label-1769171' class=' answer'><span>Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457681[]' id='answer-id-1769172' class='answer   answerof-457681 ' value='1769172'   \/><label for='answer-id-1769172' id='answer-label-1769172' class=' answer'><span>Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457681[]' id='answer-id-1769173' class='answer   answerof-457681 ' value='1769173'   \/><label for='answer-id-1769173' id='answer-label-1769173' class=' answer'><span>Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457681[]' id='answer-id-1769174' class='answer   answerof-457681 ' value='1769174'   \/><label for='answer-id-1769174' id='answer-label-1769174' class=' answer'><span>Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.<\/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-457682'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='457682' \/><input type='hidden' id='answerType457682' 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-457682[]' id='answer-id-1769175' class='answer   answerof-457682 ' value='1769175'   \/><label for='answer-id-1769175' id='answer-label-1769175' class=' answer'><span>Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457682[]' id='answer-id-1769176' class='answer   answerof-457682 ' value='1769176'   \/><label for='answer-id-1769176' id='answer-label-1769176' class=' answer'><span>Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457682[]' id='answer-id-1769177' class='answer   answerof-457682 ' value='1769177'   \/><label for='answer-id-1769177' id='answer-label-1769177' class=' answer'><span>Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457682[]' id='answer-id-1769178' class='answer   answerof-457682 ' value='1769178'   \/><label for='answer-id-1769178' id='answer-label-1769178' class=' answer'><span>Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.<\/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-457683'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine. <br \/>\r<br>The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='457683' \/><input type='hidden' id='answerType457683' 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-457683[]' id='answer-id-1769179' class='answer   answerof-457683 ' value='1769179'   \/><label for='answer-id-1769179' id='answer-label-1769179' class=' answer'><span>Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457683[]' id='answer-id-1769180' class='answer   answerof-457683 ' value='1769180'   \/><label for='answer-id-1769180' id='answer-label-1769180' class=' answer'><span>Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457683[]' id='answer-id-1769181' class='answer   answerof-457683 ' value='1769181'   \/><label for='answer-id-1769181' id='answer-label-1769181' class=' answer'><span>Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457683[]' id='answer-id-1769182' class='answer   answerof-457683 ' value='1769182'   \/><label for='answer-id-1769182' id='answer-label-1769182' class=' answer'><span>Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.<\/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-457684'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required. <br \/>\r<br>What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='457684' \/><input type='hidden' id='answerType457684' 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-457684[]' id='answer-id-1769183' class='answer   answerof-457684 ' value='1769183'   \/><label for='answer-id-1769183' id='answer-label-1769183' class=' answer'><span>Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457684[]' id='answer-id-1769184' class='answer   answerof-457684 ' value='1769184'   \/><label for='answer-id-1769184' id='answer-label-1769184' class=' answer'><span>Create an Apache Spark job that uses a custom processing script on Amazon EM<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457684[]' id='answer-id-1769185' class='answer   answerof-457684 ' value='1769185'   \/><label for='answer-id-1769185' id='answer-label-1769185' class=' answer'><span>Create a SageMaker processing job by calling the SageMaker Python SD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457684[]' id='answer-id-1769186' class='answer   answerof-457684 ' value='1769186'   \/><label for='answer-id-1769186' id='answer-label-1769186' class=' answer'><span>Create a data flow in SageMaker Data Wrangler. Configure a transform step.<\/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-457685'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>Case study <br \/>\r<br>An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. <br \/>\r<br>The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. <br \/>\r<br>The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model. <br \/>\r<br>Which action will meet this requirement with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='457685' \/><input type='hidden' id='answerType457685' 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-457685[]' id='answer-id-1769187' class='answer   answerof-457685 ' value='1769187'   \/><label for='answer-id-1769187' id='answer-label-1769187' class=' answer'><span>Use AWS Glue to transform the categorical data into numerical data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457685[]' id='answer-id-1769188' class='answer   answerof-457685 ' value='1769188'   \/><label for='answer-id-1769188' id='answer-label-1769188' class=' answer'><span>Use AWS Glue to transform the numerical data into categorical data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457685[]' id='answer-id-1769189' class='answer   answerof-457685 ' value='1769189'   \/><label for='answer-id-1769189' id='answer-label-1769189' class=' answer'><span>Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457685[]' id='answer-id-1769190' class='answer   answerof-457685 ' value='1769190'   \/><label for='answer-id-1769190' id='answer-label-1769190' class=' answer'><span>Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.<\/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-457686'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain. <br \/>\r<br>Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address. <br \/>\r<br>Which update to the network configuration will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='457686' \/><input type='hidden' id='answerType457686' 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-457686[]' id='answer-id-1769191' class='answer   answerof-457686 ' value='1769191'   \/><label for='answer-id-1769191' id='answer-label-1769191' class=' answer'><span>Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457686[]' id='answer-id-1769192' class='answer   answerof-457686 ' value='1769192'   \/><label for='answer-id-1769192' id='answer-label-1769192' class=' answer'><span>Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457686[]' id='answer-id-1769193' class='answer   answerof-457686 ' value='1769193'   \/><label for='answer-id-1769193' id='answer-label-1769193' class=' answer'><span>Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457686[]' id='answer-id-1769194' class='answer   answerof-457686 ' value='1769194'   \/><label for='answer-id-1769194' id='answer-label-1769194' class=' answer'><span>Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.<\/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-457687'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>HOTSPOT<br \/>\r\n<br \/>\r\nA company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML<br \/>\r\n<br \/>\r\nengineer needs to prepare and store the data so that the company can use the data to train ML models.<br \/>\r\n<br \/>\r\nSelect and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)<br \/>\r\n<br \/>\r\n\u2022 Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.<br \/>\r\n<br \/>\r\n\u2022 Store the resulting data back in Amazon S3.<br \/>\r\n<br \/>\r\n\u2022 Use Amazon Athena to infer the schemas and available columns.<br \/>\r\n<br \/>\r\n\u2022 Use AWS Glue crawlers to infer the schemas and available columns.<br \/>\r\n<br \/>\r\n\u2022 Use AWS Glue DataBrew for data cleaning and feature engineering.<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"\u56fe\u7247 25\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2026\/02\/image001-18.jpg\" width=\"443\" height=\"271\" \/><\/div><input type='hidden' name='question_id[]' id='qID_7' value='457687' \/><input type='hidden' id='answerType457687' 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-457687[]' id='answer-id-1769195' class='answer   answerof-457687 ' value='1769195'   \/><label for='answer-id-1769195' id='answer-label-1769195' class=' answer'><span><br><img decoding=\"async\" width=443 height=271 id=\"\u56fe\u7247 24\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2026\/02\/image002-16.jpg\"><br><\/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-457688'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data. <br \/>\r<br>Which file format will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='457688' \/><input type='hidden' id='answerType457688' 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-457688[]' id='answer-id-1769196' class='answer   answerof-457688 ' value='1769196'   \/><label for='answer-id-1769196' id='answer-label-1769196' class=' answer'><span>CSV files compressed with Snappy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457688[]' id='answer-id-1769197' class='answer   answerof-457688 ' value='1769197'   \/><label for='answer-id-1769197' id='answer-label-1769197' class=' answer'><span>JSON objects in JSONL format<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457688[]' id='answer-id-1769198' class='answer   answerof-457688 ' value='1769198'   \/><label for='answer-id-1769198' id='answer-label-1769198' class=' answer'><span>JSON files compressed with gzip<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457688[]' id='answer-id-1769199' class='answer   answerof-457688 ' value='1769199'   \/><label for='answer-id-1769199' id='answer-label-1769199' class=' answer'><span>Apache Parquet files<\/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-457689'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances. <br \/>\r<br>What should the ML engineer do to MINIMIZE the communication overhead between the instances?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='457689' \/><input type='hidden' id='answerType457689' 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-457689[]' id='answer-id-1769200' class='answer   answerof-457689 ' value='1769200'   \/><label for='answer-id-1769200' id='answer-label-1769200' class=' answer'><span>Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457689[]' id='answer-id-1769201' class='answer   answerof-457689 ' value='1769201'   \/><label for='answer-id-1769201' id='answer-label-1769201' class=' answer'><span>Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457689[]' id='answer-id-1769202' class='answer   answerof-457689 ' value='1769202'   \/><label for='answer-id-1769202' id='answer-label-1769202' class=' answer'><span>Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457689[]' id='answer-id-1769203' class='answer   answerof-457689 ' value='1769203'   \/><label for='answer-id-1769203' id='answer-label-1769203' class=' answer'><span>Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.<\/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-457690'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts. <br \/>\r<br>An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources. <br \/>\r<br>Which solution will meet these requirements with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='457690' \/><input type='hidden' id='answerType457690' 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-457690[]' id='answer-id-1769204' class='answer   answerof-457690 ' value='1769204'   \/><label for='answer-id-1769204' id='answer-label-1769204' class=' answer'><span>Create code to evaluate each instance's memory and compute usage.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457690[]' id='answer-id-1769205' class='answer   answerof-457690 ' value='1769205'   \/><label for='answer-id-1769205' id='answer-label-1769205' class=' answer'><span>Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457690[]' id='answer-id-1769206' class='answer   answerof-457690 ' value='1769206'   \/><label for='answer-id-1769206' id='answer-label-1769206' class=' answer'><span>Check AWS CloudTrail event history for the creation of the resources.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457690[]' id='answer-id-1769207' class='answer   answerof-457690 ' value='1769207'   \/><label for='answer-id-1769207' id='answer-label-1769207' class=' answer'><span>Run AWS Compute Optimizer.<\/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-457691'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. <br \/>\r<br>The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational effort?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='457691' \/><input type='hidden' id='answerType457691' 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-457691[]' id='answer-id-1769208' class='answer   answerof-457691 ' value='1769208'   \/><label for='answer-id-1769208' id='answer-label-1769208' class=' answer'><span>Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457691[]' id='answer-id-1769209' class='answer   answerof-457691 ' value='1769209'   \/><label for='answer-id-1769209' id='answer-label-1769209' class=' answer'><span>Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457691[]' id='answer-id-1769210' class='answer   answerof-457691 ' value='1769210'   \/><label for='answer-id-1769210' id='answer-label-1769210' class=' answer'><span>Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. \r\nConfigure the pipeline as the target of the rule.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457691[]' id='answer-id-1769211' class='answer   answerof-457691 ' value='1769211'   \/><label for='answer-id-1769211' id='answer-label-1769211' class=' answer'><span>Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.<\/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-457692'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>An ML engineer needs to use an ML model to predict the price of apartments in a specific location. <br \/>\r<br>Which metric should the ML engineer use to evaluate the model's performance?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='457692' \/><input type='hidden' id='answerType457692' 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-457692[]' id='answer-id-1769212' class='answer   answerof-457692 ' value='1769212'   \/><label for='answer-id-1769212' id='answer-label-1769212' class=' answer'><span>Accuracy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457692[]' id='answer-id-1769213' class='answer   answerof-457692 ' value='1769213'   \/><label for='answer-id-1769213' id='answer-label-1769213' class=' answer'><span>Area Under the ROC Curve (AUC)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457692[]' id='answer-id-1769214' class='answer   answerof-457692 ' value='1769214'   \/><label for='answer-id-1769214' id='answer-label-1769214' class=' answer'><span>F1 score<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457692[]' id='answer-id-1769215' class='answer   answerof-457692 ' value='1769215'   \/><label for='answer-id-1769215' id='answer-label-1769215' class=' answer'><span>Mean absolute error (MAE)<\/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-457693'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents. <br \/>\r<br>The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras. <br \/>\r<br>Which solution will improve the model's accuracy in the LEAST amount of time?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='457693' \/><input type='hidden' id='answerType457693' 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-457693[]' id='answer-id-1769216' class='answer   answerof-457693 ' value='1769216'   \/><label for='answer-id-1769216' id='answer-label-1769216' class=' answer'><span>Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457693[]' id='answer-id-1769217' class='answer   answerof-457693 ' value='1769217'   \/><label for='answer-id-1769217' id='answer-label-1769217' class=' answer'><span>Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457693[]' id='answer-id-1769218' class='answer   answerof-457693 ' value='1769218'   \/><label for='answer-id-1769218' id='answer-label-1769218' class=' answer'><span>Recreate the training dataset by using the Data Wrangler enhance image contrast transform. \r\nSpecify the Gamma contrast option.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457693[]' id='answer-id-1769219' class='answer   answerof-457693 ' value='1769219'   \/><label for='answer-id-1769219' id='answer-label-1769219' class=' answer'><span>Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.<\/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-457694'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='457694' \/><input type='hidden' id='answerType457694' 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-457694[]' id='answer-id-1769220' class='answer   answerof-457694 ' value='1769220'   \/><label for='answer-id-1769220' id='answer-label-1769220' class=' answer'><span>Use Amazon Made to categorize the sensitive data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457694[]' id='answer-id-1769221' class='answer   answerof-457694 ' value='1769221'   \/><label for='answer-id-1769221' id='answer-label-1769221' class=' answer'><span>Prepare the data by using AWS Glue DataBrew.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457694[]' id='answer-id-1769222' class='answer   answerof-457694 ' value='1769222'   \/><label for='answer-id-1769222' id='answer-label-1769222' class=' answer'><span>Run an AWS Batch job to change the sensitive data to random values.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457694[]' id='answer-id-1769223' class='answer   answerof-457694 ' value='1769223'   \/><label for='answer-id-1769223' id='answer-label-1769223' class=' answer'><span>Run an Amazon EMR job to change the sensitive data to random values.<\/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-457695'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results. <br \/>\r<br>An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='457695' \/><input type='hidden' id='answerType457695' 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-457695[]' id='answer-id-1769224' class='answer   answerof-457695 ' value='1769224'   \/><label for='answer-id-1769224' id='answer-label-1769224' class=' answer'><span>Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457695[]' id='answer-id-1769225' class='answer   answerof-457695 ' value='1769225'   \/><label for='answer-id-1769225' id='answer-label-1769225' class=' answer'><span>Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457695[]' id='answer-id-1769226' class='answer   answerof-457695 ' value='1769226'   \/><label for='answer-id-1769226' id='answer-label-1769226' class=' answer'><span>Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457695[]' id='answer-id-1769227' class='answer   answerof-457695 ' value='1769227'   \/><label for='answer-id-1769227' id='answer-label-1769227' class=' answer'><span>Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.<\/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-457696'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. <br \/>\r<br>The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain. <br \/>\r<br>Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_16' value='457696' \/><input type='hidden' id='answerType457696' 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-457696[]' id='answer-id-1769228' class='answer   answerof-457696 ' value='1769228'   \/><label for='answer-id-1769228' id='answer-label-1769228' class=' answer'><span>The ML engineer and the Canvas user must be in separate SageMaker domains.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-457696[]' id='answer-id-1769229' class='answer   answerof-457696 ' value='1769229'   \/><label for='answer-id-1769229' id='answer-label-1769229' class=' answer'><span>The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-457696[]' id='answer-id-1769230' class='answer   answerof-457696 ' value='1769230'   \/><label for='answer-id-1769230' id='answer-label-1769230' class=' answer'><span>The model must be registered in the SageMaker Model Registry.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-457696[]' id='answer-id-1769231' class='answer   answerof-457696 ' value='1769231'   \/><label for='answer-id-1769231' id='answer-label-1769231' class=' answer'><span>The ML engineer must host the model on AWS Marketplace.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-457696[]' id='answer-id-1769232' class='answer   answerof-457696 ' value='1769232'   \/><label for='answer-id-1769232' id='answer-label-1769232' class=' answer'><span>The ML engineer must deploy the model to a SageMaker endpoint.<\/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-457697'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='457697' \/><input type='hidden' id='answerType457697' 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-457697[]' id='answer-id-1769233' class='answer   answerof-457697 ' value='1769233'   \/><label for='answer-id-1769233' id='answer-label-1769233' class=' answer'><span>Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457697[]' id='answer-id-1769234' class='answer   answerof-457697 ' value='1769234'   \/><label for='answer-id-1769234' id='answer-label-1769234' class=' answer'><span>Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457697[]' id='answer-id-1769235' class='answer   answerof-457697 ' value='1769235'   \/><label for='answer-id-1769235' id='answer-label-1769235' class=' answer'><span>Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457697[]' id='answer-id-1769236' class='answer   answerof-457697 ' value='1769236'   \/><label for='answer-id-1769236' id='answer-label-1769236' class=' answer'><span>Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.<\/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-457698'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 \u0422\u0412 of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker. <br \/>\r<br>An ML engineer must make the training data accessible for ML models that are in the SageMaker environment. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='457698' \/><input type='hidden' id='answerType457698' 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-457698[]' id='answer-id-1769237' class='answer   answerof-457698 ' value='1769237'   \/><label for='answer-id-1769237' id='answer-label-1769237' class=' answer'><span>Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457698[]' id='answer-id-1769238' class='answer   answerof-457698 ' value='1769238'   \/><label for='answer-id-1769238' id='answer-label-1769238' class=' answer'><span>Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457698[]' id='answer-id-1769239' class='answer   answerof-457698 ' value='1769239'   \/><label for='answer-id-1769239' id='answer-label-1769239' class=' answer'><span>Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457698[]' id='answer-id-1769240' class='answer   answerof-457698 ' value='1769240'   \/><label for='answer-id-1769240' id='answer-label-1769240' class=' answer'><span>Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.<\/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-457699'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. <br \/>\r<br>Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='457699' \/><input type='hidden' id='answerType457699' 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-457699[]' id='answer-id-1769241' class='answer   answerof-457699 ' value='1769241'   \/><label for='answer-id-1769241' id='answer-label-1769241' class=' answer'><span>AWS::SageMaker::Model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457699[]' id='answer-id-1769242' class='answer   answerof-457699 ' value='1769242'   \/><label for='answer-id-1769242' id='answer-label-1769242' class=' answer'><span>AWS::SageMaker::Endpoint<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457699[]' id='answer-id-1769243' class='answer   answerof-457699 ' value='1769243'   \/><label for='answer-id-1769243' id='answer-label-1769243' class=' answer'><span>AWS::SageMaker::NotebookInstance<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457699[]' id='answer-id-1769244' class='answer   answerof-457699 ' value='1769244'   \/><label for='answer-id-1769244' id='answer-label-1769244' class=' answer'><span>AWS::SageMaker::Pipeline<\/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-457700'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>Case study <br \/>\r<br>An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. <br \/>\r<br>The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. <br \/>\r<br>Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data. <br \/>\r<br>Which solution will meet this requirement with the LEAST operational effort?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='457700' \/><input type='hidden' id='answerType457700' 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-457700[]' id='answer-id-1769245' class='answer   answerof-457700 ' value='1769245'   \/><label for='answer-id-1769245' id='answer-label-1769245' class=' answer'><span>Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457700[]' id='answer-id-1769246' class='answer   answerof-457700 ' value='1769246'   \/><label for='answer-id-1769246' id='answer-label-1769246' class=' answer'><span>Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457700[]' id='answer-id-1769247' class='answer   answerof-457700 ' value='1769247'   \/><label for='answer-id-1769247' id='answer-label-1769247' class=' answer'><span>Use AWS Glue DataBrew built-in features to oversample the minority class.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457700[]' id='answer-id-1769248' class='answer   answerof-457700 ' value='1769248'   \/><label for='answer-id-1769248' id='answer-label-1769248' class=' answer'><span>Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.<\/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-457701'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks. <br \/>\r<br>What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='457701' \/><input type='hidden' id='answerType457701' 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-457701[]' id='answer-id-1769249' class='answer   answerof-457701 ' value='1769249'   \/><label for='answer-id-1769249' id='answer-label-1769249' class=' answer'><span>Adjust the model's parameters and hyperparameters.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457701[]' id='answer-id-1769250' class='answer   answerof-457701 ' value='1769250'   \/><label for='answer-id-1769250' id='answer-label-1769250' class=' answer'><span>Initiate a manual Model Monitor job that uses the most recent production data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457701[]' id='answer-id-1769251' class='answer   answerof-457701 ' value='1769251'   \/><label for='answer-id-1769251' id='answer-label-1769251' class=' answer'><span>Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457701[]' id='answer-id-1769252' class='answer   answerof-457701 ' value='1769252'   \/><label for='answer-id-1769252' id='answer-label-1769252' class=' answer'><span>Include additional data in the existing training set for the model. Retrain and redeploy the model.<\/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-457702'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='457702' \/><input type='hidden' id='answerType457702' 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-457702[]' id='answer-id-1769253' class='answer   answerof-457702 ' value='1769253'   \/><label for='answer-id-1769253' id='answer-label-1769253' class=' answer'><span>Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. \r\nUse the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457702[]' id='answer-id-1769254' class='answer   answerof-457702 ' value='1769254'   \/><label for='answer-id-1769254' id='answer-label-1769254' class=' answer'><span>Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457702[]' id='answer-id-1769255' class='answer   answerof-457702 ' value='1769255'   \/><label for='answer-id-1769255' id='answer-label-1769255' class=' answer'><span>Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457702[]' id='answer-id-1769256' class='answer   answerof-457702 ' value='1769256'   \/><label for='answer-id-1769256' id='answer-label-1769256' class=' answer'><span>Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.<\/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-457703'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. <br \/>\r<br>The ML engineer needs to copy the model to Account \u0412 in the same Region. <br \/>\r<br>Which solution will meet this requirement with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='457703' \/><input type='hidden' id='answerType457703' 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-457703[]' id='answer-id-1769257' class='answer   answerof-457703 ' value='1769257'   \/><label for='answer-id-1769257' id='answer-label-1769257' class=' answer'><span>Use Amazon S3 to make a copy of the model. Transfer the copy to Account<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457703[]' id='answer-id-1769258' class='answer   answerof-457703 ' value='1769258'   \/><label for='answer-id-1769258' id='answer-label-1769258' class=' answer'><span>Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457703[]' id='answer-id-1769259' class='answer   answerof-457703 ' value='1769259'   \/><label for='answer-id-1769259' id='answer-label-1769259' class=' answer'><span>Use AWS DataSync to replicate the model from Account A to Account<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457703[]' id='answer-id-1769260' class='answer   answerof-457703 ' value='1769260'   \/><label for='answer-id-1769260' id='answer-label-1769260' class=' answer'><span>Create an AWS Site-to-Site VPN connection between Account A and Account \u0412 to transfer the model.<\/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-457704'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance. <br \/>\r<br>The company needs to centralize management of the team's permissions. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='457704' \/><input type='hidden' id='answerType457704' 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-457704[]' id='answer-id-1769261' class='answer   answerof-457704 ' value='1769261'   \/><label for='answer-id-1769261' id='answer-label-1769261' class=' answer'><span>Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457704[]' id='answer-id-1769262' class='answer   answerof-457704 ' value='1769262'   \/><label for='answer-id-1769262' id='answer-label-1769262' class=' answer'><span>Create a single IAM group. Add the data scientists to the group. Associate the group with each \r\nnotebook instance that the team uses.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457704[]' id='answer-id-1769263' class='answer   answerof-457704 ' value='1769263'   \/><label for='answer-id-1769263' id='answer-label-1769263' class=' answer'><span>Create a single IAM user. Attach the Administrator Access AWS managed IAM policy to the user. \r\nConfigure each notebook instance to use the IAM user.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457704[]' id='answer-id-1769264' class='answer   answerof-457704 ' value='1769264'   \/><label for='answer-id-1769264' id='answer-label-1769264' class=' answer'><span>Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the Administrator Access AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.<\/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-457705'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS. <br \/>\r<br>Which solution will meet these requirements with the LEAST effort?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='457705' \/><input type='hidden' id='answerType457705' 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-457705[]' id='answer-id-1769265' class='answer   answerof-457705 ' value='1769265'   \/><label for='answer-id-1769265' id='answer-label-1769265' class=' answer'><span>Use SageMaker built-in algorithms to train the proprietary datasets.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457705[]' id='answer-id-1769266' class='answer   answerof-457705 ' value='1769266'   \/><label for='answer-id-1769266' id='answer-label-1769266' class=' answer'><span>Use SageMaker script mode and premade images for ML frameworks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457705[]' id='answer-id-1769267' class='answer   answerof-457705 ' value='1769267'   \/><label for='answer-id-1769267' id='answer-label-1769267' class=' answer'><span>Build a container on AWS that includes custom packages and a choice of ML frameworks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457705[]' id='answer-id-1769268' class='answer   answerof-457705 ' value='1769268'   \/><label for='answer-id-1769268' id='answer-label-1769268' class=' answer'><span>Purchase similar production models through AWS Marketplace.<\/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-457706'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='457706' \/><input type='hidden' id='answerType457706' 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-457706[]' id='answer-id-1769269' class='answer   answerof-457706 ' value='1769269'   \/><label for='answer-id-1769269' id='answer-label-1769269' class=' answer'><span>Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457706[]' id='answer-id-1769270' class='answer   answerof-457706 ' value='1769270'   \/><label for='answer-id-1769270' id='answer-label-1769270' class=' answer'><span>Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457706[]' id='answer-id-1769271' class='answer   answerof-457706 ' value='1769271'   \/><label for='answer-id-1769271' id='answer-label-1769271' class=' answer'><span>Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457706[]' id='answer-id-1769272' class='answer   answerof-457706 ' value='1769272'   \/><label for='answer-id-1769272' id='answer-label-1769272' class=' answer'><span>Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.<\/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-457707'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>Case Study <br \/>\r<br>A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. <br \/>\r<br>The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. <br \/>\r<br>The company is experimenting with consecutive training jobs. <br \/>\r<br>How can the company MINIMIZE infrastructure startup times for these jobs?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='457707' \/><input type='hidden' id='answerType457707' 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-457707[]' id='answer-id-1769273' class='answer   answerof-457707 ' value='1769273'   \/><label for='answer-id-1769273' id='answer-label-1769273' class=' answer'><span>Use Managed Spot Training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457707[]' id='answer-id-1769274' class='answer   answerof-457707 ' value='1769274'   \/><label for='answer-id-1769274' id='answer-label-1769274' class=' answer'><span>Use SageMaker managed warm pools.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457707[]' id='answer-id-1769275' class='answer   answerof-457707 ' value='1769275'   \/><label for='answer-id-1769275' id='answer-label-1769275' class=' answer'><span>Use SageMaker Training Compiler.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457707[]' id='answer-id-1769276' class='answer   answerof-457707 ' value='1769276'   \/><label for='answer-id-1769276' id='answer-label-1769276' class=' answer'><span>Use the SageMaker distributed data parallelism (SMDDP) library.<\/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-457708'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>HOTSPOT<br \/>\r\n<br \/>\r\nAn ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features.<br \/>\r\n<br \/>\r\nThe ML engineer will use the following feature engineering techniques to estimate the prices of the homes:<br \/>\r\n<br \/>\r\n\u2022 Feature splitting<br \/>\r\n<br \/>\r\n\u2022 Logarithmic transformation<br \/>\r\n<br \/>\r\n\u2022 One-hot encoding<br \/>\r\n<br \/>\r\n\u2022 Standardized distribution<br \/>\r\n<br \/>\r\nSelect the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"\u56fe\u7247 17\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2026\/02\/image009-11.jpg\" width=\"497\" height=\"283\" \/><\/div><input type='hidden' name='question_id[]' id='qID_28' value='457708' \/><input type='hidden' id='answerType457708' 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-457708[]' id='answer-id-1769277' class='answer   answerof-457708 ' value='1769277'   \/><label for='answer-id-1769277' id='answer-label-1769277' class=' answer'><span><br><img decoding=\"async\" width=497 height=283 id=\"\u56fe\u7247 16\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2026\/02\/image010-10.jpg\"><br><\/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-457709'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months. <br \/>\r<br>Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?<\/div><input type='hidden' name='question_id[]' id='qID_29' value='457709' \/><input type='hidden' id='answerType457709' 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-457709[]' id='answer-id-1769278' class='answer   answerof-457709 ' value='1769278'   \/><label for='answer-id-1769278' id='answer-label-1769278' class=' answer'><span>Spot Instances<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457709[]' id='answer-id-1769279' class='answer   answerof-457709 ' value='1769279'   \/><label for='answer-id-1769279' id='answer-label-1769279' class=' answer'><span>Reserved Instances<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457709[]' id='answer-id-1769280' class='answer   answerof-457709 ' value='1769280'   \/><label for='answer-id-1769280' id='answer-label-1769280' class=' answer'><span>On-Demand Instances<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457709[]' id='answer-id-1769281' class='answer   answerof-457709 ' value='1769281'   \/><label for='answer-id-1769281' id='answer-label-1769281' class=' answer'><span>Dedicated Instances<\/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-457710'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>Case Study <br \/>\r<br>A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. <br \/>\r<br>The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. <br \/>\r<br>The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='457710' \/><input type='hidden' id='answerType457710' 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-457710[]' id='answer-id-1769282' class='answer   answerof-457710 ' value='1769282'   \/><label for='answer-id-1769282' id='answer-label-1769282' class=' answer'><span>Use SageMaker Experiments to facilitate the approval process during model registration.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457710[]' id='answer-id-1769283' class='answer   answerof-457710 ' value='1769283'   \/><label for='answer-id-1769283' id='answer-label-1769283' class=' answer'><span>Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457710[]' id='answer-id-1769284' class='answer   answerof-457710 ' value='1769284'   \/><label for='answer-id-1769284' id='answer-label-1769284' class=' answer'><span>Use SageMaker Model Monitor to evaluate the performance of the model and to manage the \r\napproval.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457710[]' id='answer-id-1769285' class='answer   answerof-457710 ' value='1769285'   \/><label for='answer-id-1769285' id='answer-label-1769285' class=' answer'><span>Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to &quot;Approved.&quot;<\/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-457711'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support. <br \/>\r<br>Which modeling approach should the company use to meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_31' value='457711' \/><input type='hidden' id='answerType457711' 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-457711[]' id='answer-id-1769286' class='answer   answerof-457711 ' value='1769286'   \/><label for='answer-id-1769286' id='answer-label-1769286' class=' answer'><span>Anomaly detection<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457711[]' id='answer-id-1769287' class='answer   answerof-457711 ' value='1769287'   \/><label for='answer-id-1769287' id='answer-label-1769287' class=' answer'><span>Linear regression<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457711[]' id='answer-id-1769288' class='answer   answerof-457711 ' value='1769288'   \/><label for='answer-id-1769288' id='answer-label-1769288' class=' answer'><span>Logistic regression<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457711[]' id='answer-id-1769289' class='answer   answerof-457711 ' value='1769289'   \/><label for='answer-id-1769289' id='answer-label-1769289' class=' answer'><span>Semantic segmentation<\/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-457712'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate. <br \/>\r<br>During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions. <br \/>\r<br>What should the ML engineer do to improve the fraud detection for new transactions?<\/div><input type='hidden' name='question_id[]' id='qID_32' value='457712' \/><input type='hidden' id='answerType457712' 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-457712[]' id='answer-id-1769290' class='answer   answerof-457712 ' value='1769290'   \/><label for='answer-id-1769290' id='answer-label-1769290' class=' answer'><span>Increase the learning rate.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457712[]' id='answer-id-1769291' class='answer   answerof-457712 ' value='1769291'   \/><label for='answer-id-1769291' id='answer-label-1769291' class=' answer'><span>Remove some irrelevant features from the training dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457712[]' id='answer-id-1769292' class='answer   answerof-457712 ' value='1769292'   \/><label for='answer-id-1769292' id='answer-label-1769292' class=' answer'><span>Increase the value of the max_depth hyperparameter.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457712[]' id='answer-id-1769293' class='answer   answerof-457712 ' value='1769293'   \/><label for='answer-id-1769293' id='answer-label-1769293' class=' answer'><span>Decrease the value of the max_depth hyperparameter.<\/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-457713'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>Case study <br \/>\r<br>An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. <br \/>\r<br>The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. <br \/>\r<br>The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model. <br \/>\r<br>Which algorithm should the ML engineer use to meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='457713' \/><input type='hidden' id='answerType457713' 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-457713[]' id='answer-id-1769294' class='answer   answerof-457713 ' value='1769294'   \/><label for='answer-id-1769294' id='answer-label-1769294' class=' answer'><span>LightGBM<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457713[]' id='answer-id-1769295' class='answer   answerof-457713 ' value='1769295'   \/><label for='answer-id-1769295' id='answer-label-1769295' class=' answer'><span>Linear learner<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457713[]' id='answer-id-1769296' class='answer   answerof-457713 ' value='1769296'   \/><label for='answer-id-1769296' id='answer-label-1769296' class=' answer'><span>\u041a-means clustering<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457713[]' id='answer-id-1769297' class='answer   answerof-457713 ' value='1769297'   \/><label for='answer-id-1769297' id='answer-label-1769297' class=' answer'><span>Neural Topic Model (NTM)<\/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-457714'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>Case study <br \/>\r<br>An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. <br \/>\r<br>The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. <br \/>\r<br>After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='457714' \/><input type='hidden' id='answerType457714' 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-457714[]' id='answer-id-1769298' class='answer   answerof-457714 ' value='1769298'   \/><label for='answer-id-1769298' id='answer-label-1769298' class=' answer'><span>Use Amazon Athena to automatically detect the anomalies and to visualize the result.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457714[]' id='answer-id-1769299' class='answer   answerof-457714 ' value='1769299'   \/><label for='answer-id-1769299' id='answer-label-1769299' class=' answer'><span>Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457714[]' id='answer-id-1769300' class='answer   answerof-457714 ' value='1769300'   \/><label for='answer-id-1769300' id='answer-label-1769300' class=' answer'><span>Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457714[]' id='answer-id-1769301' class='answer   answerof-457714 ' value='1769301'   \/><label for='answer-id-1769301' id='answer-label-1769301' class=' answer'><span>Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.<\/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-457715'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>Case study <br \/>\r<br>An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. <br \/>\r<br>The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. <br \/>\r<br>Which AWS service or feature can aggregate the data from the various data sources?<\/div><input type='hidden' name='question_id[]' id='qID_35' value='457715' \/><input type='hidden' id='answerType457715' 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-457715[]' id='answer-id-1769302' class='answer   answerof-457715 ' value='1769302'   \/><label for='answer-id-1769302' id='answer-label-1769302' class=' answer'><span>Amazon EMR Spark jobs<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457715[]' id='answer-id-1769303' class='answer   answerof-457715 ' value='1769303'   \/><label for='answer-id-1769303' id='answer-label-1769303' class=' answer'><span>Amazon Kinesis Data Streams<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457715[]' id='answer-id-1769304' class='answer   answerof-457715 ' value='1769304'   \/><label for='answer-id-1769304' id='answer-label-1769304' class=' answer'><span>Amazon DynamoDB<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457715[]' id='answer-id-1769305' class='answer   answerof-457715 ' value='1769305'   \/><label for='answer-id-1769305' id='answer-label-1769305' class=' answer'><span>AWS Lake Formation<\/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-457716'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_36' value='457716' \/><input type='hidden' id='answerType457716' 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-457716[]' id='answer-id-1769306' class='answer   answerof-457716 ' value='1769306'   \/><label for='answer-id-1769306' id='answer-label-1769306' class=' answer'><span>Set up SageMaker Debugger and create a custom rule.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457716[]' id='answer-id-1769307' class='answer   answerof-457716 ' value='1769307'   \/><label for='answer-id-1769307' id='answer-label-1769307' class=' answer'><span>Set up blue\/green deployments with all-at-once traffic shifting.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457716[]' id='answer-id-1769308' class='answer   answerof-457716 ' value='1769308'   \/><label for='answer-id-1769308' id='answer-label-1769308' class=' answer'><span>Set up blue\/green deployments with canary traffic shifting.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457716[]' id='answer-id-1769309' class='answer   answerof-457716 ' value='1769309'   \/><label for='answer-id-1769309' id='answer-label-1769309' class=' answer'><span>Set up shadow testing with a shadow variant of the new model.<\/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-457717'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter. <br \/>\r<br>What should the company do to MINIMIZE false positives?<\/div><input type='hidden' name='question_id[]' id='qID_37' value='457717' \/><input type='hidden' id='answerType457717' 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-457717[]' id='answer-id-1769310' class='answer   answerof-457717 ' value='1769310'   \/><label for='answer-id-1769310' id='answer-label-1769310' class=' answer'><span>Set the value of the weight decay hyperparameter to zero.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457717[]' id='answer-id-1769311' class='answer   answerof-457717 ' value='1769311'   \/><label for='answer-id-1769311' id='answer-label-1769311' class=' answer'><span>Increase the number of training epochs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457717[]' id='answer-id-1769312' class='answer   answerof-457717 ' value='1769312'   \/><label for='answer-id-1769312' id='answer-label-1769312' class=' answer'><span>Increase the value of the target_precision hyperparameter.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457717[]' id='answer-id-1769313' class='answer   answerof-457717 ' value='1769313'   \/><label for='answer-id-1769313' id='answer-label-1769313' class=' answer'><span>Change the value of the predictorjype hyperparameter to regressor.<\/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-457718'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. <br \/>\r<br>The models are in model groups in the SageMaker Model Registry. <br \/>\r<br>The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_38' value='457718' \/><input type='hidden' id='answerType457718' 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-457718[]' id='answer-id-1769314' class='answer   answerof-457718 ' value='1769314'   \/><label for='answer-id-1769314' id='answer-label-1769314' class=' answer'><span>Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457718[]' id='answer-id-1769315' class='answer   answerof-457718 ' value='1769315'   \/><label for='answer-id-1769315' id='answer-label-1769315' class=' answer'><span>Create a model group for each category. Move the existing models into these category model groups.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457718[]' id='answer-id-1769316' class='answer   answerof-457718 ' value='1769316'   \/><label for='answer-id-1769316' id='answer-label-1769316' class=' answer'><span>Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457718[]' id='answer-id-1769317' class='answer   answerof-457718 ' value='1769317'   \/><label for='answer-id-1769317' id='answer-label-1769317' class=' answer'><span>Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.<\/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-457719'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. <br \/>\r<br>The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements. <br \/>\r<br>Which metric should the ML engineer use for the model recalibration?<\/div><input type='hidden' name='question_id[]' id='qID_39' value='457719' \/><input type='hidden' id='answerType457719' 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-457719[]' id='answer-id-1769318' class='answer   answerof-457719 ' value='1769318'   \/><label for='answer-id-1769318' id='answer-label-1769318' class=' answer'><span>Accuracy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457719[]' id='answer-id-1769319' class='answer   answerof-457719 ' value='1769319'   \/><label for='answer-id-1769319' id='answer-label-1769319' class=' answer'><span>Precision<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457719[]' id='answer-id-1769320' class='answer   answerof-457719 ' value='1769320'   \/><label for='answer-id-1769320' id='answer-label-1769320' class=' answer'><span>Recall<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457719[]' id='answer-id-1769321' class='answer   answerof-457719 ' value='1769321'   \/><label for='answer-id-1769321' id='answer-label-1769321' class=' answer'><span>Specificity<\/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-457720'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>A company has a large collection of chat recordings from customer interactions after a product <br \/>\r<br>release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product. <br \/>\r<br>Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='457720' \/><input type='hidden' id='answerType457720' 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-457720[]' id='answer-id-1769322' class='answer   answerof-457720 ' value='1769322'   \/><label for='answer-id-1769322' id='answer-label-1769322' class=' answer'><span>Use Amazon Rekognition to analyze sentiments of the chat conversations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457720[]' id='answer-id-1769323' class='answer   answerof-457720 ' value='1769323'   \/><label for='answer-id-1769323' id='answer-label-1769323' class=' answer'><span>Train a Naive Bayes classifier to analyze sentiments of the chat conversations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457720[]' id='answer-id-1769324' class='answer   answerof-457720 ' value='1769324'   \/><label for='answer-id-1769324' id='answer-label-1769324' class=' answer'><span>Use Amazon Comprehend to analyze sentiments of the chat conversations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-457720[]' id='answer-id-1769325' class='answer   answerof-457720 ' value='1769325'   \/><label for='answer-id-1769325' id='answer-label-1769325' class=' answer'><span>Use random forests to classify sentiments of the chat conversations.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-41'>\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=\"watuPROButtons11663\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; 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   \t \n<\/script>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<p><strong>What are Amazon MLA-C01 exam dumps?<\/strong><\/p>\n\n\n\n<p>Amazon MLA-C01 exam dumps from DumpsBase are practice questions designed to help you understand the format, difficulty level, and types of questions they may encounter in the actual AWS Certified Machine Learning Engineer &#8211; Associate certification exam.<\/p>\n\n\n\n<p><strong>Does DumpsBase provide MLA-C01 PDF and practice exam software?<\/strong><\/p>\n\n\n\n<p>Yes. DumpsBase offers MLA-C01 PDF files for convenient study, along with practice exam software that accurately simulates the real certification exam environment.<\/p>\n\n\n\n<p><strong>Are the Amazon MLA-C01 questions updated regularly?<\/strong><\/p>\n\n\n\n<p>Yes. Free updates are provided for up to one year after purchase. If Amazon updates the MLA-C01 exam, you will receive the latest practice questions during your active update period.<\/p>\n\n\n\n<p><strong>Why should I choose DumpsBase for Amazon MLA-C01 questions?<\/strong><\/p>\n\n\n\n<p>DumpsBase offers realistic Amazon MLA-C01 questions, easy-to-use PDF files, practice test software, performance tracking, and free updates to ensure you prepare with absolute confidence.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Download the latest study materials from DumpsBase to prepare more efficiently for the AWS Certified Machine Learning Engineer \u2013 Associate (MLA-C01) exam. We have updated the dumps to V9.02, offering 200+ MLA-C01 exam questions to help you understand the latest ML Engineer exam topics. DumpsBase provides an affordable, highly accurate pathway to get familiar with [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[175,15702],"tags":[21487,21488],"class_list":["post-129070","post","type-post","status-publish","format-standard","hentry","category-amazon","category-aws-certified-associate","tag-mla-c01","tag-mla-c01-updated-dumps"],"_links":{"self":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/129070","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=129070"}],"version-history":[{"count":1,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/129070\/revisions"}],"predecessor-version":[{"id":129071,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/129070\/revisions\/129071"}],"wp:attachment":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/media?parent=129070"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/categories?post=129070"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/tags?post=129070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}