{"id":112918,"date":"2025-10-29T06:47:07","date_gmt":"2025-10-29T06:47:07","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=112918"},"modified":"2025-11-03T08:24:32","modified_gmt":"2025-11-03T08:24:32","slug":"mls-c01-dumps-v13-02-for-aws-certified-machine-learning-specialty-exam-preparation-read-mls-c01-free-dumps-part-1-q1-q40-first","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/mls-c01-dumps-v13-02-for-aws-certified-machine-learning-specialty-exam-preparation-read-mls-c01-free-dumps-part-1-q1-q40-first.html","title":{"rendered":"MLS-C01 Dumps (V13.02) for AWS Certified Machine Learning &#8211; Specialty Exam Preparation: Read MLS-C01 Free Dumps (Part 1, Q1-Q40) First"},"content":{"rendered":"<p>AWS upgraded its certifications recently:<\/p>\n<ol>\n<li>Launching the new AWS Certified Generative AI Developer \u2013 Professional certification<\/li>\n<li>Planning to retire AWS Certified Machine Learning \u2013 Specialty (MLS-C01) on March 31, 2026<\/li>\n<li>Updating the AWS Certified Security \u2013 Specialty (SCS-C03) on November 18, 2025<\/li>\n<\/ol>\n<p>So if you are planning to take the MLS-C01 exam, make sure that you can complete it before the retirement. We at DumpsBase consistently update the MLS-C01 dumps, aligned with the latest exam syllabus, to ensure your study materials remain current and accurate, avoiding outdated information that could interfere with your preparation. The current version of the MLS-C01 dumps is V13.02 with 330 practice exam questions and answers. They are verified by a professional team to ensure question quality and answer accuracy. By learning the MLS-C01 dumps (V13.02), you will be able to confidently face exam challenges and successfully obtain your AWS Certified Machine Learning \u2013 Specialty certification.<\/p>\n<p><!-- notionvc: 843cdfb4-ba45-4da6-8318-da4c7ce372ac --><\/p>\n<h2>Before getting the MLS-C01 dumps (V13.02), you can read our <span style=\"background-color: #99cc00;\"><em>MLS-C01 free dumps (Part 1, Q1-Q40)<\/em><\/span> first:<\/h2>\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam10288\" 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-10288\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-10288\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-408080'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. <br \/>\r<br>Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='408080' \/><input type='hidden' id='answerType408080' 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-408080[]' id='answer-id-1581743' class='answer   answerof-408080 ' value='1581743'   \/><label for='answer-id-1581743' id='answer-label-1581743' class=' answer'><span>Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408080[]' id='answer-id-1581744' class='answer   answerof-408080 ' value='1581744'   \/><label for='answer-id-1581744' id='answer-label-1581744' class=' answer'><span>Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408080[]' id='answer-id-1581745' class='answer   answerof-408080 ' value='1581745'   \/><label for='answer-id-1581745' id='answer-label-1581745' class=' answer'><span>Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408080[]' id='answer-id-1581746' class='answer   answerof-408080 ' value='1581746'   \/><label for='answer-id-1581746' id='answer-label-1581746' class=' answer'><span>Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.<\/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-408081'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size. <br \/>\r<br>What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='408081' \/><input type='hidden' id='answerType408081' 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-408081[]' id='answer-id-1581747' class='answer   answerof-408081 ' value='1581747'   \/><label for='answer-id-1581747' id='answer-label-1581747' class=' answer'><span>1 shards<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408081[]' id='answer-id-1581748' class='answer   answerof-408081 ' value='1581748'   \/><label for='answer-id-1581748' id='answer-label-1581748' class=' answer'><span>10 shards<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408081[]' id='answer-id-1581749' class='answer   answerof-408081 ' value='1581749'   \/><label for='answer-id-1581749' id='answer-label-1581749' class=' answer'><span>100 shards<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408081[]' id='answer-id-1581750' class='answer   answerof-408081 ' value='1581750'   \/><label for='answer-id-1581750' id='answer-label-1581750' class=' answer'><span>1,000 shards<\/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-408082'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations. <br \/>\r<br>Which solution should the Specialist recommend?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='408082' \/><input type='hidden' id='answerType408082' 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-408082[]' id='answer-id-1581751' class='answer   answerof-408082 ' value='1581751'   \/><label for='answer-id-1581751' id='answer-label-1581751' class=' answer'><span>Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408082[]' id='answer-id-1581752' class='answer   answerof-408082 ' value='1581752'   \/><label for='answer-id-1581752' id='answer-label-1581752' class=' answer'><span>A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408082[]' id='answer-id-1581753' class='answer   answerof-408082 ' value='1581753'   \/><label for='answer-id-1581753' id='answer-label-1581753' class=' answer'><span>Collaborative filtering based on user interactions and correlations to identify patterns in the customer database<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408082[]' id='answer-id-1581754' class='answer   answerof-408082 ' value='1581754'   \/><label for='answer-id-1581754' id='answer-label-1581754' class=' answer'><span>Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database<\/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-408083'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data <br \/>\r<br>Which AWS service should the Data Scientist use?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='408083' \/><input type='hidden' id='answerType408083' 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-408083[]' id='answer-id-1581755' class='answer   answerof-408083 ' value='1581755'   \/><label for='answer-id-1581755' id='answer-label-1581755' class=' answer'><span>Amazon Athena<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408083[]' id='answer-id-1581756' class='answer   answerof-408083 ' value='1581756'   \/><label for='answer-id-1581756' id='answer-label-1581756' class=' answer'><span>Amazon Redshift Spectrum<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408083[]' id='answer-id-1581757' class='answer   answerof-408083 ' value='1581757'   \/><label for='answer-id-1581757' id='answer-label-1581757' class=' answer'><span>AWS Glue<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408083[]' id='answer-id-1581758' class='answer   answerof-408083 ' value='1581758'   \/><label for='answer-id-1581758' id='answer-label-1581758' class=' answer'><span>Amazon QuickSight<\/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-408084'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data. <br \/>\r<br>Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='408084' \/><input type='hidden' id='answerType408084' 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-408084[]' id='answer-id-1581759' class='answer   answerof-408084 ' value='1581759'   \/><label for='answer-id-1581759' id='answer-label-1581759' class=' answer'><span>Listwise deletion<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408084[]' id='answer-id-1581760' class='answer   answerof-408084 ' value='1581760'   \/><label for='answer-id-1581760' id='answer-label-1581760' class=' answer'><span>Last observation carried forward<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408084[]' id='answer-id-1581761' class='answer   answerof-408084 ' value='1581761'   \/><label for='answer-id-1581761' id='answer-label-1581761' class=' answer'><span>Multiple imputation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408084[]' id='answer-id-1581762' class='answer   answerof-408084 ' value='1581762'   \/><label for='answer-id-1581762' id='answer-label-1581762' class=' answer'><span>Mean substitution<\/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-408085'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10] <br \/>\r<br><br><img decoding=\"async\" width=644 height=382 id=\"\u56fe\u7247 28\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/06\/image001-35.jpg\"><br><br \/>\r<br>Considering the graph, what is a reasonable selection for the optimal choice of k?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='408085' \/><input type='hidden' id='answerType408085' 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-408085[]' id='answer-id-1581763' class='answer   answerof-408085 ' value='1581763'   \/><label for='answer-id-1581763' id='answer-label-1581763' class=' answer'><span>1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408085[]' id='answer-id-1581764' class='answer   answerof-408085 ' value='1581764'   \/><label for='answer-id-1581764' id='answer-label-1581764' class=' answer'><span>4<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408085[]' id='answer-id-1581765' class='answer   answerof-408085 ' value='1581765'   \/><label for='answer-id-1581765' id='answer-label-1581765' class=' answer'><span>7<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408085[]' id='answer-id-1581766' class='answer   answerof-408085 ' value='1581766'   \/><label for='answer-id-1581766' id='answer-label-1581766' class=' answer'><span>10<\/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-408086'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team. <br \/>\r<br>Which solution requires the LEAST coding effort?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='408086' \/><input type='hidden' id='answerType408086' 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-408086[]' id='answer-id-1581767' class='answer   answerof-408086 ' value='1581767'   \/><label for='answer-id-1581767' id='answer-label-1581767' class=' answer'><span>Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408086[]' id='answer-id-1581768' class='answer   answerof-408086 ' value='1581768'   \/><label for='answer-id-1581768' id='answer-label-1581768' class=' answer'><span>Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408086[]' id='answer-id-1581769' class='answer   answerof-408086 ' value='1581769'   \/><label for='answer-id-1581769' id='answer-label-1581769' class=' answer'><span>Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408086[]' id='answer-id-1581770' class='answer   answerof-408086 ' value='1581770'   \/><label for='answer-id-1581770' id='answer-label-1581770' class=' answer'><span>Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.<\/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-408087'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset. <br \/>\r<br>What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='408087' \/><input type='hidden' id='answerType408087' 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-408087[]' id='answer-id-1581771' class='answer   answerof-408087 ' value='1581771'   \/><label for='answer-id-1581771' id='answer-label-1581771' class=' answer'><span>Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408087[]' id='answer-id-1581772' class='answer   answerof-408087 ' value='1581772'   \/><label for='answer-id-1581772' id='answer-label-1581772' class=' answer'><span>Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408087[]' id='answer-id-1581773' class='answer   answerof-408087 ' value='1581773'   \/><label for='answer-id-1581773' id='answer-label-1581773' class=' answer'><span>Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408087[]' id='answer-id-1581774' class='answer   answerof-408087 ' value='1581774'   \/><label for='answer-id-1581774' id='answer-label-1581774' class=' answer'><span>Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.<\/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-408088'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question. <br \/>\r<br>A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='408088' \/><input type='hidden' id='answerType408088' 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-408088[]' id='answer-id-1581775' class='answer   answerof-408088 ' value='1581775'   \/><label for='answer-id-1581775' id='answer-label-1581775' class=' answer'><span>Perform one-hot encoding on every possible option for each question of the survey.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408088[]' id='answer-id-1581776' class='answer   answerof-408088 ' value='1581776'   \/><label for='answer-id-1581776' id='answer-label-1581776' class=' answer'><span>Perform binning on all the answers each respondent selected for each question.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408088[]' id='answer-id-1581777' class='answer   answerof-408088 ' value='1581777'   \/><label for='answer-id-1581777' id='answer-label-1581777' class=' answer'><span>Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408088[]' id='answer-id-1581778' class='answer   answerof-408088 ' value='1581778'   \/><label for='answer-id-1581778' id='answer-label-1581778' class=' answer'><span>Use Amazon Textract to create numeric features for each set of possible responses.<\/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-408089'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A finance company has collected stock return data for 5.000 publicly traded companies. A financial analyst has a dataset that contains 2.000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='408089' \/><input type='hidden' id='answerType408089' 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-408089[]' id='answer-id-1581779' class='answer   answerof-408089 ' value='1581779'   \/><label for='answer-id-1581779' id='answer-label-1581779' class=' answer'><span>Use the linear learner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408089[]' id='answer-id-1581780' class='answer   answerof-408089 ' value='1581780'   \/><label for='answer-id-1581780' id='answer-label-1581780' class=' answer'><span>Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408089[]' id='answer-id-1581781' class='answer   answerof-408089 ' value='1581781'   \/><label for='answer-id-1581781' id='answer-label-1581781' class=' answer'><span>Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. \r\nIdentify the most predictive features based on the quick model's feature importance scores.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408089[]' id='answer-id-1581782' class='answer   answerof-408089 ' value='1581782'   \/><label for='answer-id-1581782' id='answer-label-1581782' class=' answer'><span>Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.<\/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-408090'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them. <br \/>\r<br>Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_11' value='408090' \/><input type='hidden' id='answerType408090' 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-408090[]' id='answer-id-1581783' class='answer   answerof-408090 ' value='1581783'   \/><label for='answer-id-1581783' id='answer-label-1581783' class=' answer'><span>Amazon SageMaker seq2seq algorithm<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408090[]' id='answer-id-1581784' class='answer   answerof-408090 ' value='1581784'   \/><label for='answer-id-1581784' id='answer-label-1581784' class=' answer'><span>Amazon SageMaker BlazingText algorithm in Skip-gram mode<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408090[]' id='answer-id-1581785' class='answer   answerof-408090 ' value='1581785'   \/><label for='answer-id-1581785' id='answer-label-1581785' class=' answer'><span>Amazon SageMaker Object2Vec algorithm<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408090[]' id='answer-id-1581786' class='answer   answerof-408090 ' value='1581786'   \/><label for='answer-id-1581786' id='answer-label-1581786' class=' answer'><span>Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408090[]' id='answer-id-1581787' class='answer   answerof-408090 ' value='1581787'   \/><label for='answer-id-1581787' id='answer-label-1581787' class=' answer'><span>Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)<\/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-408091'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation. <br \/>\r<br>Which solution will meet these requirements with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='408091' \/><input type='hidden' id='answerType408091' 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-408091[]' id='answer-id-1581788' class='answer   answerof-408091 ' value='1581788'   \/><label for='answer-id-1581788' id='answer-label-1581788' class=' answer'><span>Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408091[]' id='answer-id-1581789' class='answer   answerof-408091 ' value='1581789'   \/><label for='answer-id-1581789' id='answer-label-1581789' class=' answer'><span>Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408091[]' id='answer-id-1581790' class='answer   answerof-408091 ' value='1581790'   \/><label for='answer-id-1581790' id='answer-label-1581790' class=' answer'><span>Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408091[]' id='answer-id-1581791' class='answer   answerof-408091 ' value='1581791'   \/><label for='answer-id-1581791' id='answer-label-1581791' class=' answer'><span>Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.<\/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-408092'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance. <br \/>\r<br>How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='408092' \/><input type='hidden' id='answerType408092' 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-408092[]' id='answer-id-1581792' class='answer   answerof-408092 ' value='1581792'   \/><label for='answer-id-1581792' id='answer-label-1581792' class=' answer'><span>Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408092[]' id='answer-id-1581793' class='answer   answerof-408092 ' value='1581793'   \/><label for='answer-id-1581793' id='answer-label-1581793' class=' answer'><span>Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the \/etc\/init directory of each Amazon SageMaker notebook instance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408092[]' id='answer-id-1581794' class='answer   answerof-408092 ' value='1581794'   \/><label for='answer-id-1581794' id='answer-label-1581794' class=' answer'><span>Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408092[]' id='answer-id-1581795' class='answer   answerof-408092 ' value='1581795'   \/><label for='answer-id-1581795' id='answer-label-1581795' class=' answer'><span>Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.<\/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-408093'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A Machine Learning Specialist wants to determine the appropriate SageMaker Variant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5 <br \/>\r<br>Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMaker variant invocations Per instance setting?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='408093' \/><input type='hidden' id='answerType408093' 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-408093[]' id='answer-id-1581796' class='answer   answerof-408093 ' value='1581796'   \/><label for='answer-id-1581796' id='answer-label-1581796' class=' answer'><span>10<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408093[]' id='answer-id-1581797' class='answer   answerof-408093 ' value='1581797'   \/><label for='answer-id-1581797' id='answer-label-1581797' class=' answer'><span>30<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408093[]' id='answer-id-1581798' class='answer   answerof-408093 ' value='1581798'   \/><label for='answer-id-1581798' id='answer-label-1581798' class=' answer'><span>600<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408093[]' id='answer-id-1581799' class='answer   answerof-408093 ' value='1581799'   \/><label for='answer-id-1581799' id='answer-label-1581799' class=' answer'><span>2,400<\/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-408094'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='408094' \/><input type='hidden' id='answerType408094' 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-408094[]' id='answer-id-1581800' class='answer   answerof-408094 ' value='1581800'   \/><label for='answer-id-1581800' id='answer-label-1581800' class=' answer'><span>Amazon S3 with S3 Cross-Region Replication (CRR)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408094[]' id='answer-id-1581801' class='answer   answerof-408094 ' value='1581801'   \/><label for='answer-id-1581801' id='answer-label-1581801' class=' answer'><span>Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408094[]' id='answer-id-1581802' class='answer   answerof-408094 ' value='1581802'   \/><label for='answer-id-1581802' id='answer-label-1581802' class=' answer'><span>Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408094[]' id='answer-id-1581803' class='answer   answerof-408094 ' value='1581803'   \/><label for='answer-id-1581803' id='answer-label-1581803' class=' answer'><span>AWS Storage Gateway Volume Gateway<\/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-408095'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types. <br \/>\r<br>How should the company prepare the data for the model to improve the model's accuracy?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='408095' \/><input type='hidden' id='answerType408095' 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-408095[]' id='answer-id-1581804' class='answer   answerof-408095 ' value='1581804'   \/><label for='answer-id-1581804' id='answer-label-1581804' class=' answer'><span>Adjust the class weight to account for each machine type.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408095[]' id='answer-id-1581805' class='answer   answerof-408095 ' value='1581805'   \/><label for='answer-id-1581805' id='answer-label-1581805' class=' answer'><span>Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408095[]' id='answer-id-1581806' class='answer   answerof-408095 ' value='1581806'   \/><label for='answer-id-1581806' id='answer-label-1581806' class=' answer'><span>Undersample the non-failure events. Stratify the non-failure events by machine type.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408095[]' id='answer-id-1581807' class='answer   answerof-408095 ' value='1581807'   \/><label for='answer-id-1581807' id='answer-label-1581807' class=' answer'><span>Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).<\/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-408096'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance. <br \/>\r<br>Which approach allows the Specialist to use all the data to train the model?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='408096' \/><input type='hidden' id='answerType408096' 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-408096[]' id='answer-id-1581808' class='answer   answerof-408096 ' value='1581808'   \/><label for='answer-id-1581808' id='answer-label-1581808' class=' answer'><span>Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Initiate a SageMaker training job using thefull dataset from the S3 bucket using Pipe input mode.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408096[]' id='answer-id-1581809' class='answer   answerof-408096 ' value='1581809'   \/><label for='answer-id-1581809' id='answer-label-1581809' class=' answer'><span>Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstance. Train on a small amount of the data to verify the training code and hyperparameters. Go back toAmazon SageMaker and train using the full dataset<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408096[]' id='answer-id-1581810' class='answer   answerof-408096 ' value='1581810'   \/><label for='answer-id-1581810' id='answer-label-1581810' class=' answer'><span>Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408096[]' id='answer-id-1581811' class='answer   answerof-408096 ' value='1581811'   \/><label for='answer-id-1581811' id='answer-label-1581811' class=' answer'><span>Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the \r\ntrainingcode is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with anAWS Deep Learning AMI and attach the S3 bucket to train the full dataset.<\/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-408097'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket. <br \/>\r<br>How should the data scientist accomplish this?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='408097' \/><input type='hidden' id='answerType408097' 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-408097[]' id='answer-id-1581812' class='answer   answerof-408097 ' value='1581812'   \/><label for='answer-id-1581812' id='answer-label-1581812' class=' answer'><span>Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408097[]' id='answer-id-1581813' class='answer   answerof-408097 ' value='1581813'   \/><label for='answer-id-1581813' id='answer-label-1581813' class=' answer'><span>Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408097[]' id='answer-id-1581814' class='answer   answerof-408097 ' value='1581814'   \/><label for='answer-id-1581814' id='answer-label-1581814' class=' answer'><span>Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408097[]' id='answer-id-1581815' class='answer   answerof-408097 ' value='1581815'   \/><label for='answer-id-1581815' id='answer-label-1581815' class=' answer'><span>Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.<\/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-408098'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance. <br \/>\r<br>What type of machine learning model should be used?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='408098' \/><input type='hidden' id='answerType408098' 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-408098[]' id='answer-id-1581816' class='answer   answerof-408098 ' value='1581816'   \/><label for='answer-id-1581816' id='answer-label-1581816' class=' answer'><span>Classification month-to-month using supervised learning of the 200 categories based on claim contents.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408098[]' id='answer-id-1581817' class='answer   answerof-408098 ' value='1581817'   \/><label for='answer-id-1581817' id='answer-label-1581817' class=' answer'><span>Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408098[]' id='answer-id-1581818' class='answer   answerof-408098 ' value='1581818'   \/><label for='answer-id-1581818' id='answer-label-1581818' class=' answer'><span>Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408098[]' id='answer-id-1581819' class='answer   answerof-408098 ' value='1581819'   \/><label for='answer-id-1581819' id='answer-label-1581819' class=' answer'><span>Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.<\/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-408099'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis <br \/>\r<br>What is the MOST efficient way to accomplish these tasks?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='408099' \/><input type='hidden' id='answerType408099' 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-408099[]' id='answer-id-1581820' class='answer   answerof-408099 ' value='1581820'   \/><label for='answer-id-1581820' id='answer-label-1581820' class=' answer'><span>Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408099[]' id='answer-id-1581821' class='answer   answerof-408099 ' value='1581821'   \/><label for='answer-id-1581821' id='answer-label-1581821' class=' answer'><span>Ingest the data into Apache Spark Streaming using Amazon EM<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408099[]' id='answer-id-1581822' class='answer   answerof-408099 ' value='1581822'   \/><label for='answer-id-1581822' id='answer-label-1581822' class=' answer'><span>and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408099[]' id='answer-id-1581823' class='answer   answerof-408099 ' value='1581823'   \/><label for='answer-id-1581823' id='answer-label-1581823' class=' answer'><span>Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408099[]' id='answer-id-1581824' class='answer   answerof-408099 ' value='1581824'   \/><label for='answer-id-1581824' id='answer-label-1581824' class=' answer'><span>Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data<\/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-408100'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS. <br \/>\r<br>How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='408100' \/><input type='hidden' id='answerType408100' 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-408100[]' id='answer-id-1581825' class='answer   answerof-408100 ' value='1581825'   \/><label for='answer-id-1581825' id='answer-label-1581825' class=' answer'><span>Define security group(s) to allow all HTTP inbound\/outbound traffic and assign those security group(s) tothe Amazon SageMaker notebook instance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408100[]' id='answer-id-1581826' class='answer   answerof-408100 ' value='1581826'   \/><label for='answer-id-1581826' id='answer-label-1581826' class=' answer'><span>\u0421onfigure the Amazon SageMaker notebook instance to have access to the VP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408100[]' id='answer-id-1581827' class='answer   answerof-408100 ' value='1581827'   \/><label for='answer-id-1581827' id='answer-label-1581827' class=' answer'><span>Grant permission in theKMS key policy to the notebook\u2019s KMS role.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408100[]' id='answer-id-1581828' class='answer   answerof-408100 ' value='1581828'   \/><label for='answer-id-1581828' id='answer-label-1581828' class=' answer'><span>Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. \r\nGrantpermission in the KMS key policy to that role.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408100[]' id='answer-id-1581829' class='answer   answerof-408100 ' value='1581829'   \/><label for='answer-id-1581829' id='answer-label-1581829' class=' answer'><span>Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebookinstance.<\/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-408101'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>A Data Engineer needs to build a model using a dataset containing customer credit card information. <br \/>\r<br>How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='408101' \/><input type='hidden' id='answerType408101' 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-408101[]' id='answer-id-1581830' class='answer   answerof-408101 ' value='1581830'   \/><label for='answer-id-1581830' id='answer-label-1581830' class=' answer'><span>Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMakerinstance in a VP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408101[]' id='answer-id-1581831' class='answer   answerof-408101 ' value='1581831'   \/><label for='answer-id-1581831' id='answer-label-1581831' class=' answer'><span>Use the SageMaker DeepAR algorithm to randomize the credit card numbers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408101[]' id='answer-id-1581832' class='answer   answerof-408101 ' value='1581832'   \/><label for='answer-id-1581832' id='answer-label-1581832' class=' answer'><span>Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automaticallydiscard credit card numbers and insert fake credit card numbers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408101[]' id='answer-id-1581833' class='answer   answerof-408101 ' value='1581833'   \/><label for='answer-id-1581833' id='answer-label-1581833' class=' answer'><span>Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMakerinstance in a VP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408101[]' id='answer-id-1581834' class='answer   answerof-408101 ' value='1581834'   \/><label for='answer-id-1581834' id='answer-label-1581834' class=' answer'><span>Use the SageMaker principal component analysis (PCA) algorithm to reduce the lengthof the credit card numbers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408101[]' id='answer-id-1581835' class='answer   answerof-408101 ' value='1581835'   \/><label for='answer-id-1581835' id='answer-label-1581835' class=' answer'><span>Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.<\/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-408102'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes <br \/>\r<br>Which function will produce the desired output?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='408102' \/><input type='hidden' id='answerType408102' 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-408102[]' id='answer-id-1581836' class='answer   answerof-408102 ' value='1581836'   \/><label for='answer-id-1581836' id='answer-label-1581836' class=' answer'><span>Dropout<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408102[]' id='answer-id-1581837' class='answer   answerof-408102 ' value='1581837'   \/><label for='answer-id-1581837' id='answer-label-1581837' class=' answer'><span>Smooth L1 loss<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408102[]' id='answer-id-1581838' class='answer   answerof-408102 ' value='1581838'   \/><label for='answer-id-1581838' id='answer-label-1581838' class=' answer'><span>Softmax<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408102[]' id='answer-id-1581839' class='answer   answerof-408102 ' value='1581839'   \/><label for='answer-id-1581839' id='answer-label-1581839' class=' answer'><span>Rectified linear units (ReLU)<\/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-408103'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy. <br \/>\r<br>Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='408103' \/><input type='hidden' id='answerType408103' 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-408103[]' id='answer-id-1581840' class='answer   answerof-408103 ' value='1581840'   \/><label for='answer-id-1581840' id='answer-label-1581840' class=' answer'><span>m5 4xlarge (general purpose)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408103[]' id='answer-id-1581841' class='answer   answerof-408103 ' value='1581841'   \/><label for='answer-id-1581841' id='answer-label-1581841' class=' answer'><span>r5.2xlarge (memory optimized)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408103[]' id='answer-id-1581842' class='answer   answerof-408103 ' value='1581842'   \/><label for='answer-id-1581842' id='answer-label-1581842' class=' answer'><span>p3.2xlarge (GPU accelerated computing)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408103[]' id='answer-id-1581843' class='answer   answerof-408103 ' value='1581843'   \/><label for='answer-id-1581843' id='answer-label-1581843' class=' answer'><span>p3 8xlarge (GPU accelerated computing)<\/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-408104'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as &quot;a,&quot; &quot;an,\u201d and &quot;the&quot; as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords. <br \/>\r<br>What should the data scientist do to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='408104' \/><input type='hidden' id='answerType408104' 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-408104[]' id='answer-id-1581844' class='answer   answerof-408104 ' value='1581844'   \/><label for='answer-id-1581844' id='answer-label-1581844' class=' answer'><span>Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408104[]' id='answer-id-1581845' class='answer   answerof-408104 ' value='1581845'   \/><label for='answer-id-1581845' id='answer-label-1581845' class=' answer'><span>Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408104[]' id='answer-id-1581846' class='answer   answerof-408104 ' value='1581846'   \/><label for='answer-id-1581846' id='answer-label-1581846' class=' answer'><span>Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408104[]' id='answer-id-1581847' class='answer   answerof-408104 ' value='1581847'   \/><label for='answer-id-1581847' id='answer-label-1581847' class=' answer'><span>Remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.<\/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-408105'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car. <br \/>\r<br>Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='408105' \/><input type='hidden' id='answerType408105' 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-408105[]' id='answer-id-1581848' class='answer   answerof-408105 ' value='1581848'   \/><label for='answer-id-1581848' id='answer-label-1581848' class=' answer'><span>Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408105[]' id='answer-id-1581849' class='answer   answerof-408105 ' value='1581849'   \/><label for='answer-id-1581849' id='answer-label-1581849' class=' answer'><span>Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408105[]' id='answer-id-1581850' class='answer   answerof-408105 ' value='1581850'   \/><label for='answer-id-1581850' id='answer-label-1581850' class=' answer'><span>Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408105[]' id='answer-id-1581851' class='answer   answerof-408105 ' value='1581851'   \/><label for='answer-id-1581851' id='answer-label-1581851' class=' answer'><span>Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.<\/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-408106'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model <br \/>\r<br>What should the Specialist do to prepare the data for model training'?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='408106' \/><input type='hidden' id='answerType408106' 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-408106[]' id='answer-id-1581852' class='answer   answerof-408106 ' value='1581852'   \/><label for='answer-id-1581852' id='answer-label-1581852' class=' answer'><span>Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408106[]' id='answer-id-1581853' class='answer   answerof-408106 ' value='1581853'   \/><label for='answer-id-1581853' id='answer-label-1581853' class=' answer'><span>Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408106[]' id='answer-id-1581854' class='answer   answerof-408106 ' value='1581854'   \/><label for='answer-id-1581854' id='answer-label-1581854' class=' answer'><span>Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408106[]' id='answer-id-1581855' class='answer   answerof-408106 ' value='1581855'   \/><label for='answer-id-1581855' id='answer-label-1581855' class=' answer'><span>Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.<\/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-408107'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations. <br \/>\r<br>Which solution should a machine learning specialist implement to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_28' value='408107' \/><input type='hidden' id='answerType408107' 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-408107[]' id='answer-id-1581856' class='answer   answerof-408107 ' value='1581856'   \/><label for='answer-id-1581856' id='answer-label-1581856' class=' answer'><span>Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408107[]' id='answer-id-1581857' class='answer   answerof-408107 ' value='1581857'   \/><label for='answer-id-1581857' id='answer-label-1581857' class=' answer'><span>Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408107[]' id='answer-id-1581858' class='answer   answerof-408107 ' value='1581858'   \/><label for='answer-id-1581858' id='answer-label-1581858' class=' answer'><span>Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408107[]' id='answer-id-1581859' class='answer   answerof-408107 ' value='1581859'   \/><label for='answer-id-1581859' id='answer-label-1581859' class=' answer'><span>Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.<\/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-408108'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price. <br \/>\r<br>Which techniques should the company use for feature selection? (Choose three.)<\/div><input type='hidden' name='question_id[]' id='qID_29' value='408108' \/><input type='hidden' id='answerType408108' 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-408108[]' id='answer-id-1581860' class='answer   answerof-408108 ' value='1581860'   \/><label for='answer-id-1581860' id='answer-label-1581860' class=' answer'><span>Data scaling with standardization and normalization<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408108[]' id='answer-id-1581861' class='answer   answerof-408108 ' value='1581861'   \/><label for='answer-id-1581861' id='answer-label-1581861' class=' answer'><span>Correlation plot with heat maps<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408108[]' id='answer-id-1581862' class='answer   answerof-408108 ' value='1581862'   \/><label for='answer-id-1581862' id='answer-label-1581862' class=' answer'><span>Data binning<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408108[]' id='answer-id-1581863' class='answer   answerof-408108 ' value='1581863'   \/><label for='answer-id-1581863' id='answer-label-1581863' class=' answer'><span>Univariate selection<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408108[]' id='answer-id-1581864' class='answer   answerof-408108 ' value='1581864'   \/><label for='answer-id-1581864' id='answer-label-1581864' class=' answer'><span>Feature importance with a tree-based classifier<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408108[]' id='answer-id-1581865' class='answer   answerof-408108 ' value='1581865'   \/><label for='answer-id-1581865' id='answer-label-1581865' class=' answer'><span>Data augmentation<\/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-408109'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>A company wants to segment a large group of customers into subgroups based on shared characteristics. The company\u2019s data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use. <br \/>\r<br>Which data visualization approach will MOST accurately determine the optimal value of k?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='408109' \/><input type='hidden' id='answerType408109' 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-408109[]' id='answer-id-1581866' class='answer   answerof-408109 ' value='1581866'   \/><label for='answer-id-1581866' id='answer-label-1581866' class=' answer'><span>Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408109[]' id='answer-id-1581867' class='answer   answerof-408109 ' value='1581867'   \/><label for='answer-id-1581867' id='answer-label-1581867' class=' answer'><span>Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408109[]' id='answer-id-1581868' class='answer   answerof-408109 ' value='1581868'   \/><label for='answer-id-1581868' id='answer-label-1581868' class=' answer'><span>Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408109[]' id='answer-id-1581869' class='answer   answerof-408109 ' value='1581869'   \/><label for='answer-id-1581869' id='answer-label-1581869' class=' answer'><span>Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.<\/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-408110'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10.000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels * 224 pixels. After several training runs, the model is overfitting on the training data. <br \/>\r<br>Which actions should the ML specialist take to address this problem? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_31' value='408110' \/><input type='hidden' id='answerType408110' 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-408110[]' id='answer-id-1581870' class='answer   answerof-408110 ' value='1581870'   \/><label for='answer-id-1581870' id='answer-label-1581870' class=' answer'><span>Use Amazon SageMaker Ground Truth to label the unlabeled images<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408110[]' id='answer-id-1581871' class='answer   answerof-408110 ' value='1581871'   \/><label for='answer-id-1581871' id='answer-label-1581871' class=' answer'><span>Use image preprocessing to transform the images into grayscale images.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408110[]' id='answer-id-1581872' class='answer   answerof-408110 ' value='1581872'   \/><label for='answer-id-1581872' id='answer-label-1581872' class=' answer'><span>Use data augmentation to rotate and translate the labeled images.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408110[]' id='answer-id-1581873' class='answer   answerof-408110 ' value='1581873'   \/><label for='answer-id-1581873' id='answer-label-1581873' class=' answer'><span>Replace the activation of the last layer with a sigmoid.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408110[]' id='answer-id-1581874' class='answer   answerof-408110 ' value='1581874'   \/><label for='answer-id-1581874' id='answer-label-1581874' class=' answer'><span>Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.<\/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-408111'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only <br \/>\r<br>The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases <br \/>\r<br>Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?<\/div><input type='hidden' name='question_id[]' id='qID_32' value='408111' \/><input type='hidden' id='answerType408111' 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-408111[]' id='answer-id-1581875' class='answer   answerof-408111 ' value='1581875'   \/><label for='answer-id-1581875' id='answer-label-1581875' class=' answer'><span>Increase the randomization of training data in the mini-batches used in training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408111[]' id='answer-id-1581876' class='answer   answerof-408111 ' value='1581876'   \/><label for='answer-id-1581876' id='answer-label-1581876' class=' answer'><span>Allocate a higher proportion of the overall data to the training dataset<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408111[]' id='answer-id-1581877' class='answer   answerof-408111 ' value='1581877'   \/><label for='answer-id-1581877' id='answer-label-1581877' class=' answer'><span>Apply L1 or L2 regularization and dropouts to the training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408111[]' id='answer-id-1581878' class='answer   answerof-408111 ' value='1581878'   \/><label for='answer-id-1581878' id='answer-label-1581878' class=' answer'><span>Reduce the number of layers and units (or neurons) from the deep learning network.<\/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-408112'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier: <br \/>\r<br>Total number of images available = 1,000 Test set images = 100 (constant test set) <br \/>\r<br>The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners. <br \/>\r<br>Which techniques can be used by the ML Specialist to improve this specific test error?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='408112' \/><input type='hidden' id='answerType408112' 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-408112[]' id='answer-id-1581879' class='answer   answerof-408112 ' value='1581879'   \/><label for='answer-id-1581879' id='answer-label-1581879' class=' answer'><span>Increase the training data by adding variation in rotation for training images.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408112[]' id='answer-id-1581880' class='answer   answerof-408112 ' value='1581880'   \/><label for='answer-id-1581880' id='answer-label-1581880' class=' answer'><span>Increase the number of epochs for model training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408112[]' id='answer-id-1581881' class='answer   answerof-408112 ' value='1581881'   \/><label for='answer-id-1581881' id='answer-label-1581881' class=' answer'><span>Increase the number of layers for the neural network.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408112[]' id='answer-id-1581882' class='answer   answerof-408112 ' value='1581882'   \/><label for='answer-id-1581882' id='answer-label-1581882' class=' answer'><span>Increase the dropout rate for the second-to-last layer.<\/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-408113'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes. <br \/>\r<br>Which prior probability distribution should the ML Specialist use for this variable?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='408113' \/><input type='hidden' id='answerType408113' 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-408113[]' id='answer-id-1581883' class='answer   answerof-408113 ' value='1581883'   \/><label for='answer-id-1581883' id='answer-label-1581883' class=' answer'><span>Poisson distribution<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408113[]' id='answer-id-1581884' class='answer   answerof-408113 ' value='1581884'   \/><label for='answer-id-1581884' id='answer-label-1581884' class=' answer'><span>Uniform distribution<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408113[]' id='answer-id-1581885' class='answer   answerof-408113 ' value='1581885'   \/><label for='answer-id-1581885' id='answer-label-1581885' class=' answer'><span>Normal distribution<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408113[]' id='answer-id-1581886' class='answer   answerof-408113 ' value='1581886'   \/><label for='answer-id-1581886' id='answer-label-1581886' class=' answer'><span>Binomial distribution<\/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-408114'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository. <br \/>\r<br>Which combination of steps will meet these requirements? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_35' value='408114' \/><input type='hidden' id='answerType408114' 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-408114[]' id='answer-id-1581887' class='answer   answerof-408114 ' value='1581887'   \/><label for='answer-id-1581887' id='answer-label-1581887' class=' answer'><span>Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408114[]' id='answer-id-1581888' class='answer   answerof-408114 ' value='1581888'   \/><label for='answer-id-1581888' id='answer-label-1581888' class=' answer'><span>Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408114[]' id='answer-id-1581889' class='answer   answerof-408114 ' value='1581889'   \/><label for='answer-id-1581889' id='answer-label-1581889' class=' answer'><span>Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408114[]' id='answer-id-1581890' class='answer   answerof-408114 ' value='1581890'   \/><label for='answer-id-1581890' id='answer-label-1581890' class=' answer'><span>Set up S3 replication between the development S3 buckets and the integration and production S3 buckets.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408114[]' id='answer-id-1581891' class='answer   answerof-408114 ' value='1581891'   \/><label for='answer-id-1581891' id='answer-label-1581891' class=' answer'><span>Create an AWS PrivateLink endpoint in the development account for SageMaker.<\/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-408115'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification. <br \/>\r<br>Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of &quot;Sample Yogurt&quot; in the document of customer comments, then &quot;Sample Yogurt&quot; should be classified as &quot;yogurt,&quot; &quot;snack,&quot; and &quot;dairy product.&quot; <br \/>\r<br>The team is using Amazon Comprehend to train the model and must complete the project as soon as possible. <br \/>\r<br>Which functionality of Amazon Comprehend should the team use to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_36' value='408115' \/><input type='hidden' id='answerType408115' 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-408115[]' id='answer-id-1581892' class='answer   answerof-408115 ' value='1581892'   \/><label for='answer-id-1581892' id='answer-label-1581892' class=' answer'><span>Custom classification with multi-class mode<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408115[]' id='answer-id-1581893' class='answer   answerof-408115 ' value='1581893'   \/><label for='answer-id-1581893' id='answer-label-1581893' class=' answer'><span>Custom classification with multi-label mode<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408115[]' id='answer-id-1581894' class='answer   answerof-408115 ' value='1581894'   \/><label for='answer-id-1581894' id='answer-label-1581894' class=' answer'><span>Custom entity recognition<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408115[]' id='answer-id-1581895' class='answer   answerof-408115 ' value='1581895'   \/><label for='answer-id-1581895' id='answer-label-1581895' class=' answer'><span>Built-in models<\/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-408116'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance. <br \/>\r<br>The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. <br \/>\r<br>Which solution will result in the MOST cost savings?<\/div><input type='hidden' name='question_id[]' id='qID_37' value='408116' \/><input type='hidden' id='answerType408116' 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-408116[]' id='answer-id-1581896' class='answer   answerof-408116 ' value='1581896'   \/><label for='answer-id-1581896' id='answer-label-1581896' class=' answer'><span>Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408116[]' id='answer-id-1581897' class='answer   answerof-408116 ' value='1581897'   \/><label for='answer-id-1581897' id='answer-label-1581897' class=' answer'><span>Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408116[]' id='answer-id-1581898' class='answer   answerof-408116 ' value='1581898'   \/><label for='answer-id-1581898' id='answer-label-1581898' class=' answer'><span>Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml. r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408116[]' id='answer-id-1581899' class='answer   answerof-408116 ' value='1581899'   \/><label for='answer-id-1581899' id='answer-label-1581899' class=' answer'><span>Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.<\/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-408117'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models <br \/>\r<br>During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images. <br \/>\r<br>Which of the following should be used to resolve this issue? (Select TWO)<\/div><input type='hidden' name='question_id[]' id='qID_38' value='408117' \/><input type='hidden' id='answerType408117' 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-408117[]' id='answer-id-1581900' class='answer   answerof-408117 ' value='1581900'   \/><label for='answer-id-1581900' id='answer-label-1581900' class=' answer'><span>Add vanishing gradient to the model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408117[]' id='answer-id-1581901' class='answer   answerof-408117 ' value='1581901'   \/><label for='answer-id-1581901' id='answer-label-1581901' class=' answer'><span>Perform data augmentation on the training data<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408117[]' id='answer-id-1581902' class='answer   answerof-408117 ' value='1581902'   \/><label for='answer-id-1581902' id='answer-label-1581902' class=' answer'><span>Make the neural network architecture complex.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408117[]' id='answer-id-1581903' class='answer   answerof-408117 ' value='1581903'   \/><label for='answer-id-1581903' id='answer-label-1581903' class=' answer'><span>Use gradient checking in the model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408117[]' id='answer-id-1581904' class='answer   answerof-408117 ' value='1581904'   \/><label for='answer-id-1581904' id='answer-label-1581904' class=' answer'><span>Add L2 regularization to the model<\/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-408118'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products. <br \/>\r<br>Which solution will meet these requirements with the MOST operational efficiency?<\/div><input type='hidden' name='question_id[]' id='qID_39' value='408118' \/><input type='hidden' id='answerType408118' 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-408118[]' id='answer-id-1581905' class='answer   answerof-408118 ' value='1581905'   \/><label for='answer-id-1581905' id='answer-label-1581905' class=' answer'><span>Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408118[]' id='answer-id-1581906' class='answer   answerof-408118 ' value='1581906'   \/><label for='answer-id-1581906' id='answer-label-1581906' class=' answer'><span>Tokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408118[]' id='answer-id-1581907' class='answer   answerof-408118 ' value='1581907'   \/><label for='answer-id-1581907' id='answer-label-1581907' class=' answer'><span>Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408118[]' id='answer-id-1581908' class='answer   answerof-408118 ' value='1581908'   \/><label for='answer-id-1581908' id='answer-label-1581908' class=' answer'><span>Train an Amazon SageMaker Blazing Text model to generate the product categories.<\/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-408119'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. <br \/>\r<br>The files contain a timestamp variable in the following format: <br \/>\r<br>March 1st, 2020, 08:14pm - <br \/>\r<br>There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis. <br \/>\r<br>Which solution requires the LEAST operational overhead to create a new dataset with the added features?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='408119' \/><input type='hidden' id='answerType408119' 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-408119[]' id='answer-id-1581909' class='answer   answerof-408119 ' value='1581909'   \/><label for='answer-id-1581909' id='answer-label-1581909' class=' answer'><span>Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408119[]' id='answer-id-1581910' class='answer   answerof-408119 ' value='1581910'   \/><label for='answer-id-1581910' id='answer-label-1581910' class=' answer'><span>Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408119[]' id='answer-id-1581911' class='answer   answerof-408119 ' value='1581911'   \/><label for='answer-id-1581911' id='answer-label-1581911' class=' answer'><span>Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date\/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408119[]' id='answer-id-1581912' class='answer   answerof-408119 ' value='1581912'   \/><label for='answer-id-1581912' id='answer-label-1581912' class=' answer'><span>Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.<\/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=\"watuPROButtons10288\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; Previous\" onclick=\"WatuPRO.nextQuestion(event, 'previous');\"\/><\/div>\t\t  \t\t  \t\t   \n\t\t   \t  \t\t<div><input type=\"button\" name=\"action\" class=\"watupro-submit-button\" onclick=\"WatuPRO.submitResult(event)\" id=\"action-button\" value=\"View Results\"  \/>\n\t\t<\/div>\n\t\t<\/div>\n\t\t\n\t<input type=\"hidden\" name=\"quiz_id\" value=\"10288\" id=\"watuPROExamID\"\/>\n\t<input type=\"hidden\" name=\"start_time\" id=\"startTime\" value=\"2026-05-05 18:35:21\" \/>\n\t<input type=\"hidden\" name=\"start_timestamp\" id=\"startTimeStamp\" value=\"1778006121\" \/>\n\t<input type=\"hidden\" name=\"question_ids\" value=\"\" \/>\n\t<input type=\"hidden\" name=\"watupro_questions\" value=\"408080:1581743,1581744,1581745,1581746 | 408081:1581747,1581748,1581749,1581750 | 408082:1581751,1581752,1581753,1581754 | 408083:1581755,1581756,1581757,1581758 | 408084:1581759,1581760,1581761,1581762 | 408085:1581763,1581764,1581765,1581766 | 408086:1581767,1581768,1581769,1581770 | 408087:1581771,1581772,1581773,1581774 | 408088:1581775,1581776,1581777,1581778 | 408089:1581779,1581780,1581781,1581782 | 408090:1581783,1581784,1581785,1581786,1581787 | 408091:1581788,1581789,1581790,1581791 | 408092:1581792,1581793,1581794,1581795 | 408093:1581796,1581797,1581798,1581799 | 408094:1581800,1581801,1581802,1581803 | 408095:1581804,1581805,1581806,1581807 | 408096:1581808,1581809,1581810,1581811 | 408097:1581812,1581813,1581814,1581815 | 408098:1581816,1581817,1581818,1581819 | 408099:1581820,1581821,1581822,1581823,1581824 | 408100:1581825,1581826,1581827,1581828,1581829 | 408101:1581830,1581831,1581832,1581833,1581834,1581835 | 408102:1581836,1581837,1581838,1581839 | 408103:1581840,1581841,1581842,1581843 | 408104:1581844,1581845,1581846,1581847 | 408105:1581848,1581849,1581850,1581851 | 408106:1581852,1581853,1581854,1581855 | 408107:1581856,1581857,1581858,1581859 | 408108:1581860,1581861,1581862,1581863,1581864,1581865 | 408109:1581866,1581867,1581868,1581869 | 408110:1581870,1581871,1581872,1581873,1581874 | 408111:1581875,1581876,1581877,1581878 | 408112:1581879,1581880,1581881,1581882 | 408113:1581883,1581884,1581885,1581886 | 408114:1581887,1581888,1581889,1581890,1581891 | 408115:1581892,1581893,1581894,1581895 | 408116:1581896,1581897,1581898,1581899 | 408117:1581900,1581901,1581902,1581903,1581904 | 408118:1581905,1581906,1581907,1581908 | 408119:1581909,1581910,1581911,1581912\" \/>\n\t<input type=\"hidden\" name=\"no_ajax\" value=\"0\">\t\t\t<\/form>\n\t<p>&nbsp;<\/p>\n<\/div>\n\n<script type=\"text\/javascript\">\n\/\/jQuery(document).ready(function(){\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \t\nvar question_ids = \"408080,408081,408082,408083,408084,408085,408086,408087,408088,408089,408090,408091,408092,408093,408094,408095,408096,408097,408098,408099,408100,408101,408102,408103,408104,408105,408106,408107,408108,408109,408110,408111,408112,408113,408114,408115,408116,408117,408118,408119\";\nWatuPROSettings[10288] = {};\nWatuPRO.qArr = question_ids.split(',');\nWatuPRO.exam_id = 10288;\t    \nWatuPRO.post_id = 112918;\nWatuPRO.store_progress = 0;\nWatuPRO.curCatPage = 1;\nWatuPRO.requiredIDs=\"0\".split(\",\");\nWatuPRO.hAppID = \"0.95123700 1778006121\";\nvar url = \"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/show_exam.php\";\nWatuPRO.examMode = 1;\nWatuPRO.siteURL=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-admin\/admin-ajax.php\";\nWatuPRO.emailIsNotRequired = 0;\nWatuPROIntel.init(10288);\nWatuPRO.inCategoryPages=1;});    \t \n<\/script>\n<p>&nbsp;<\/p>\n<h3>Continue to check the <a href=\"https:\/\/www.dumpsbase.com\/freedumps\/continue-to-read-mls-c01-free-dumps-part-2-q41-q80-today-trust-the-mls-c01-dumps-v13-02-are-reliable-for-preparation.html\"><span style=\"background-color: #99cc00;\"><em>MLS-C01 free dumps (Part 2, Q41-Q80) of V13.02<\/em><\/span><\/a> here.<\/h3>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AWS upgraded its certifications recently: Launching the new AWS Certified Generative AI Developer \u2013 Professional certification Planning to retire AWS Certified Machine Learning \u2013 Specialty (MLS-C01) on March 31, 2026 Updating the AWS Certified Security \u2013 Specialty (SCS-C03) on November 18, 2025 So if you are planning to take the MLS-C01 exam, make sure that [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[175,15637],"tags":[20184,17459],"class_list":["post-112918","post","type-post","status-publish","format-standard","hentry","category-amazon","category-aws-certification","tag-aws-certified-machine-learning-specialty-mls-c01","tag-mls-c01-dumps"],"_links":{"self":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/112918","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=112918"}],"version-history":[{"count":2,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/112918\/revisions"}],"predecessor-version":[{"id":113347,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/112918\/revisions\/113347"}],"wp:attachment":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/media?parent=112918"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/categories?post=112918"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/tags?post=112918"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}