{"id":113876,"date":"2025-11-12T05:54:25","date_gmt":"2025-11-12T05:54:25","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=113876"},"modified":"2025-11-12T05:54:25","modified_gmt":"2025-11-12T05:54:25","slug":"dumpsbase-mls-c01-dumps-v13-02-continue-to-read-the-mls-c01-free-dumps-part-3-q81-q120-today","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/dumpsbase-mls-c01-dumps-v13-02-continue-to-read-the-mls-c01-free-dumps-part-3-q81-q120-today.html","title":{"rendered":"DumpsBase MLS-C01 Dumps (V13.02): Continue to Read the MLS-C01 Free Dumps (Part 3, Q81-Q120) Today"},"content":{"rendered":"<p>If you are preparing for the AWS Certified Machine Learning &#8211; Specialty (MLS-C01) exam, DumpsBase offers the most updated and reliable learning resources to ensure your success. At DumpsBase, you can get the most updated MLS-C01 dumps (V13.02) that match the latest syllabus and exam structure, helping you save time and focus on what really matters. You can read our free dumps to verify the V13.02 first:<\/p>\n<ul>\n<li><a href=\"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\"><em>MLS-C01 free dumps (Part 1, Q1-Q40) of V13.02<\/em><\/a><\/li>\n<li><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\"><em>MLS-C01 free dumps (Part 2, Q41-Q80) of V13.02<\/em><\/a><\/li>\n<\/ul>\n<p>From these sample questions online, you can find that the MLS-C01 dumps (V13.02) give you the real exam questions in a real test experience, allowing you to evaluate your knowledge and identify improvement areas. You can check the reviews; thousands of professionals have already passed their MLS-C01 exam using DumpsBase&#8217;s trusted materials. If you want to check more demos, come to read and test today.<\/p>\n<h2>Continue to read our <span style=\"background-color: #ffff99;\"><em>MLS-C01 free dumps (Part 3, Q81-Q120) of V13.02 below<\/em><\/span>:<\/h2>\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam10290\" 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-10290\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-10290\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-408160'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents. <br \/>\r<br>The company\u2019s data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model\u2019s testing accuracy. <br \/>\r<br>Which process will improve the testing accuracy the MOST?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='408160' \/><input type='hidden' id='answerType408160' 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-408160[]' id='answer-id-1582086' class='answer   answerof-408160 ' value='1582086'   \/><label for='answer-id-1582086' id='answer-label-1582086' class=' answer'><span>Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408160[]' id='answer-id-1582087' class='answer   answerof-408160 ' value='1582087'   \/><label for='answer-id-1582087' id='answer-label-1582087' class=' answer'><span>Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408160[]' id='answer-id-1582088' class='answer   answerof-408160 ' value='1582088'   \/><label for='answer-id-1582088' id='answer-label-1582088' class=' answer'><span>Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408160[]' id='answer-id-1582089' class='answer   answerof-408160 ' value='1582089'   \/><label for='answer-id-1582089' id='answer-label-1582089' class=' answer'><span>Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.<\/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-408161'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches <br \/>\r<br>What actions would allow the Specialist to get relevant numerical representations?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='408161' \/><input type='hidden' id='answerType408161' 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-408161[]' id='answer-id-1582090' class='answer   answerof-408161 ' value='1582090'   \/><label for='answer-id-1582090' id='answer-label-1582090' class=' answer'><span>Reduce image resolution and use reduced resolution pixel values as features<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408161[]' id='answer-id-1582091' class='answer   answerof-408161 ' value='1582091'   \/><label for='answer-id-1582091' id='answer-label-1582091' class=' answer'><span>Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408161[]' id='answer-id-1582092' class='answer   answerof-408161 ' value='1582092'   \/><label for='answer-id-1582092' id='answer-label-1582092' class=' answer'><span>Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408161[]' id='answer-id-1582093' class='answer   answerof-408161 ' value='1582093'   \/><label for='answer-id-1582093' id='answer-label-1582093' class=' answer'><span>Average colors by channel to obtain three-dimensional representations of images.<\/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-408162'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A data scientist uses Amazon SageMaker Data Wrangler to obtain a feature summary from a dataset that the data scientist imported from Amazon S3. The data scientist notices that the prediction power <br \/>\r<br>for a dataset feature has a score of 1. <br \/>\r<br>What is the cause of the score?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='408162' \/><input type='hidden' id='answerType408162' 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-408162[]' id='answer-id-1582094' class='answer   answerof-408162 ' value='1582094'   \/><label for='answer-id-1582094' id='answer-label-1582094' class=' answer'><span>Target leakage occurred in the imported dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408162[]' id='answer-id-1582095' class='answer   answerof-408162 ' value='1582095'   \/><label for='answer-id-1582095' id='answer-label-1582095' class=' answer'><span>The data scientist did not fine-tune the training and validation split.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408162[]' id='answer-id-1582096' class='answer   answerof-408162 ' value='1582096'   \/><label for='answer-id-1582096' id='answer-label-1582096' class=' answer'><span>The SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model fit for each feature to calculate the prediction power.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408162[]' id='answer-id-1582097' class='answer   answerof-408162 ' value='1582097'   \/><label for='answer-id-1582097' id='answer-label-1582097' class=' answer'><span>The data scientist did not process the features enough to accurately calculate prediction power.<\/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-408163'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions. <br \/>\r<br>After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model. <br \/>\r<br>Which solutions will meet this requirement? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_4' value='408163' \/><input type='hidden' id='answerType408163' 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-408163[]' id='answer-id-1582098' class='answer   answerof-408163 ' value='1582098'   \/><label for='answer-id-1582098' id='answer-label-1582098' class=' answer'><span>Increase the number of passes on the existing training data. Perform more hyperparameter tuning.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408163[]' id='answer-id-1582099' class='answer   answerof-408163 ' value='1582099'   \/><label for='answer-id-1582099' id='answer-label-1582099' class=' answer'><span>Increase the amount of regularization. Use fewer feature combinations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408163[]' id='answer-id-1582100' class='answer   answerof-408163 ' value='1582100'   \/><label for='answer-id-1582100' id='answer-label-1582100' class=' answer'><span>Add new domain-specific features. Use more complex models.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408163[]' id='answer-id-1582101' class='answer   answerof-408163 ' value='1582101'   \/><label for='answer-id-1582101' id='answer-label-1582101' class=' answer'><span>Use fewer feature combinations. Decrease the number of numeric attribute bins.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408163[]' id='answer-id-1582102' class='answer   answerof-408163 ' value='1582102'   \/><label for='answer-id-1582102' id='answer-label-1582102' class=' answer'><span>Decrease the amount of training data examples. Reduce the number of passes on the existing training data.<\/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-408164'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>A growing company has a business-critical key performance indicator (KPI) for the uptime of a machine learning (ML) recommendation system. The company is using Amazon SageMaker hosting services to develop a recommendation model in a single Availability Zone within an AWS Region. A machine learning (ML) specialist must develop a solution to achieve high availability. The solution must have a recovery time objective (RTO) of 5 minutes. <br \/>\r<br>Which solution will meet these requirements with the LEAST effort?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='408164' \/><input type='hidden' id='answerType408164' 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-408164[]' id='answer-id-1582103' class='answer   answerof-408164 ' value='1582103'   \/><label for='answer-id-1582103' id='answer-label-1582103' class=' answer'><span>Deploy multiple instances for each endpoint in a VPC that spans at least two Regions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408164[]' id='answer-id-1582104' class='answer   answerof-408164 ' value='1582104'   \/><label for='answer-id-1582104' id='answer-label-1582104' class=' answer'><span>Use the SageMaker auto scaling feature for the hosted recommendation models.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408164[]' id='answer-id-1582105' class='answer   answerof-408164 ' value='1582105'   \/><label for='answer-id-1582105' id='answer-label-1582105' class=' answer'><span>Deploy multiple instances for each production endpoint in a VPC that spans at least two subnets that are in a second Availability Zone.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408164[]' id='answer-id-1582106' class='answer   answerof-408164 ' value='1582106'   \/><label for='answer-id-1582106' id='answer-label-1582106' class=' answer'><span>Frequently generate backups of the production recommendation model. Deploy the backups in a second Region.<\/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-408165'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. <br \/>\r<br>Which method should Example Corp use to split the data into a training dataset and evaluation dataset?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='408165' \/><input type='hidden' id='answerType408165' 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-408165[]' id='answer-id-1582107' class='answer   answerof-408165 ' value='1582107'   \/><label for='answer-id-1582107' id='answer-label-1582107' class=' answer'><span>Pre-split the data before uploading to Amazon S3<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408165[]' id='answer-id-1582108' class='answer   answerof-408165 ' value='1582108'   \/><label for='answer-id-1582108' id='answer-label-1582108' class=' answer'><span>Have Amazon ML split the data randomly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408165[]' id='answer-id-1582109' class='answer   answerof-408165 ' value='1582109'   \/><label for='answer-id-1582109' id='answer-label-1582109' class=' answer'><span>Have Amazon ML split the data sequentially.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408165[]' id='answer-id-1582110' class='answer   answerof-408165 ' value='1582110'   \/><label for='answer-id-1582110' id='answer-label-1582110' class=' answer'><span>Perform custom cross-validation on the data<\/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-408166'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model\u2019s accuracy. The learning rate parameter is specified in the following HPO configuration: <br \/>\r<br>During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue. <br \/>\r<br>Which solution provides the MOST accurate result?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='408166' \/><input type='hidden' id='answerType408166' 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-408166[]' id='answer-id-1582111' class='answer   answerof-408166 ' value='1582111'   \/><label for='answer-id-1582111' id='answer-label-1582111' class=' answer'><span>Modify the HPO configuration as follows: \r\n<br><img decoding=\"async\" width=432 height=126 id=\"\u56fe\u7247 2\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/06\/image003-32.jpg\"><br>\r\nSelect the most accurate hyperparameter configuration form this HPO job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408166[]' id='answer-id-1582112' class='answer   answerof-408166 ' value='1582112'   \/><label for='answer-id-1582112' id='answer-label-1582112' class=' answer'><span>Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job: \r\n[0.01, 0.1] \r\n[0.001, 0.01] \r\n[0.0001, 0.001] \r\nSelect the most accurate hyperparameter configuration form these three HPO jobs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408166[]' id='answer-id-1582113' class='answer   answerof-408166 ' value='1582113'   \/><label for='answer-id-1582113' id='answer-label-1582113' class=' answer'><span>Modify the HPO configuration as follows: \r\n<br><img decoding=\"async\" width=352 height=127 id=\"\u56fe\u7247 1\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/06\/image004-27.jpg\"><br>\r\nSelect the most accurate hyperparameter configuration form this training job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408166[]' id='answer-id-1582114' class='answer   answerof-408166 ' value='1582114'   \/><label for='answer-id-1582114' id='answer-label-1582114' class=' answer'><span>Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. \r\nDivide the number of training jobs for each HPO job by three: \r\n[0.01, 0.1] \r\n[0.001, 0.01] \r\n[0.0001, 0.001] \r\nSelect the most accurate hyperparameter configuration form these three HPO jobs.<\/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-408167'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance <br \/>\r<br>How should the records be stored in Amazon S3 to improve query performance?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='408167' \/><input type='hidden' id='answerType408167' 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-408167[]' id='answer-id-1582115' class='answer   answerof-408167 ' value='1582115'   \/><label for='answer-id-1582115' id='answer-label-1582115' class=' answer'><span>CSV files<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408167[]' id='answer-id-1582116' class='answer   answerof-408167 ' value='1582116'   \/><label for='answer-id-1582116' id='answer-label-1582116' class=' answer'><span>Parquet files<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408167[]' id='answer-id-1582117' class='answer   answerof-408167 ' value='1582117'   \/><label for='answer-id-1582117' id='answer-label-1582117' class=' answer'><span>Compressed JSON<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408167[]' id='answer-id-1582118' class='answer   answerof-408167 ' value='1582118'   \/><label for='answer-id-1582118' id='answer-label-1582118' class=' answer'><span>RecordIO<\/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-408168'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application. <br \/>\r<br>The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production. To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed <br \/>\r<br>What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='408168' \/><input type='hidden' id='answerType408168' 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-408168[]' id='answer-id-1582119' class='answer   answerof-408168 ' value='1582119'   \/><label for='answer-id-1582119' id='answer-label-1582119' class=' answer'><span>Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408168[]' id='answer-id-1582120' class='answer   answerof-408168 ' value='1582120'   \/><label for='answer-id-1582120' id='answer-label-1582120' class=' answer'><span>Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408168[]' id='answer-id-1582121' class='answer   answerof-408168 ' value='1582121'   \/><label for='answer-id-1582121' id='answer-label-1582121' class=' answer'><span>Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408168[]' id='answer-id-1582122' class='answer   answerof-408168 ' value='1582122'   \/><label for='answer-id-1582122' id='answer-label-1582122' class=' answer'><span>Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.<\/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-408169'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent. <br \/>\r<br>How should the Specialist frame this business problem?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='408169' \/><input type='hidden' id='answerType408169' 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-408169[]' id='answer-id-1582123' class='answer   answerof-408169 ' value='1582123'   \/><label for='answer-id-1582123' id='answer-label-1582123' class=' answer'><span>Streaming classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408169[]' id='answer-id-1582124' class='answer   answerof-408169 ' value='1582124'   \/><label for='answer-id-1582124' id='answer-label-1582124' class=' answer'><span>Binary classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408169[]' id='answer-id-1582125' class='answer   answerof-408169 ' value='1582125'   \/><label for='answer-id-1582125' id='answer-label-1582125' class=' answer'><span>Multi-category classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408169[]' id='answer-id-1582126' class='answer   answerof-408169 ' value='1582126'   \/><label for='answer-id-1582126' id='answer-label-1582126' class=' answer'><span>Regression classification<\/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-408170'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time. <br \/>\r<br>Which solution should the agency consider?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='408170' \/><input type='hidden' id='answerType408170' 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-408170[]' id='answer-id-1582127' class='answer   answerof-408170 ' value='1582127'   \/><label for='answer-id-1582127' id='answer-label-1582127' class=' answer'><span>Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and createa stream processor to detect faces from a collection of known employees, and alert when non-employeesare detected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408170[]' id='answer-id-1582128' class='answer   answerof-408170 ' value='1582128'   \/><label for='answer-id-1582128' id='answer-label-1582128' class=' answer'><span>Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detectfaces from a collection of known employees and alert when non-employees are detected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408170[]' id='answer-id-1582129' class='answer   answerof-408170 ' value='1582129'   \/><label for='answer-id-1582129' id='answer-label-1582129' class=' answer'><span>Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video toAmazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video andcreate a stream processor to detect faces from a collection on each stream, and alert when nonemployeesare detected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408170[]' id='answer-id-1582130' class='answer   answerof-408170 ' value='1582130'   \/><label for='answer-id-1582130' id='answer-label-1582130' class=' answer'><span>Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video toAmazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function tocapture image fragments and then call Amazon Rekognition Image to detect faces from a collection ofknown employees, and alert when non-employees are detected.<\/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-408171'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis. <br \/>\r<br>What approach would be the MOST effective to perform near-real time defect detection?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='408171' \/><input type='hidden' id='answerType408171' 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-408171[]' id='answer-id-1582131' class='answer   answerof-408171 ' value='1582131'   \/><label for='answer-id-1582131' id='answer-label-1582131' class=' answer'><span>Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from withinAWS IoT Analytics to carry out analysis for anomalies.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408171[]' id='answer-id-1582132' class='answer   answerof-408171 ' value='1582132'   \/><label for='answer-id-1582132' id='answer-label-1582132' class=' answer'><span>Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry outApache Spark ML k-means clustering to determine anomalies.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408171[]' id='answer-id-1582133' class='answer   answerof-408171 ' value='1582133'   \/><label for='answer-id-1582133' id='answer-label-1582133' class=' answer'><span>Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random CutForest (RCF) algorithm to determine anomalies.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408171[]' id='answer-id-1582134' class='answer   answerof-408171 ' value='1582134'   \/><label for='answer-id-1582134' id='answer-label-1582134' class=' answer'><span>Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for furtheranalysis.<\/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-408172'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container. <br \/>\r<br>Which action will provide the MOST secure protection?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='408172' \/><input type='hidden' id='answerType408172' 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-408172[]' id='answer-id-1582135' class='answer   answerof-408172 ' value='1582135'   \/><label for='answer-id-1582135' id='answer-label-1582135' class=' answer'><span>Remove Amazon S3 access permissions from the SageMaker execution role.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408172[]' id='answer-id-1582136' class='answer   answerof-408172 ' value='1582136'   \/><label for='answer-id-1582136' id='answer-label-1582136' class=' answer'><span>Encrypt the weights of the CNN model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408172[]' id='answer-id-1582137' class='answer   answerof-408172 ' value='1582137'   \/><label for='answer-id-1582137' id='answer-label-1582137' class=' answer'><span>Encrypt the training and validation dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408172[]' id='answer-id-1582138' class='answer   answerof-408172 ' value='1582138'   \/><label for='answer-id-1582138' id='answer-label-1582138' class=' answer'><span>Enable network isolation for training jobs.<\/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-408173'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided. <br \/>\r<br>Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='408173' \/><input type='hidden' id='answerType408173' 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-408173[]' id='answer-id-1582139' class='answer   answerof-408173 ' value='1582139'   \/><label for='answer-id-1582139' id='answer-label-1582139' class=' answer'><span>Decision tree<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408173[]' id='answer-id-1582140' class='answer   answerof-408173 ' value='1582140'   \/><label for='answer-id-1582140' id='answer-label-1582140' class=' answer'><span>Linear support vector machine (SVM)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408173[]' id='answer-id-1582141' class='answer   answerof-408173 ' value='1582141'   \/><label for='answer-id-1582141' id='answer-label-1582141' class=' answer'><span>Naive Bayesian classifier<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408173[]' id='answer-id-1582142' class='answer   answerof-408173 ' value='1582142'   \/><label for='answer-id-1582142' id='answer-label-1582142' class=' answer'><span>Single Perceptron with sigmoidal activation function<\/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-408174'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>An Amazon SageMaker notebook instance is launched into Amazon VPC. The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3. <br \/>\r<br>Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)<\/div><input type='hidden' name='question_id[]' id='qID_15' value='408174' \/><input type='hidden' id='answerType408174' 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-408174[]' id='answer-id-1582143' class='answer   answerof-408174 ' value='1582143'   \/><label for='answer-id-1582143' id='answer-label-1582143' class=' answer'><span>An AWS KMS key policy that allows access to the customer master key (CMK)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408174[]' id='answer-id-1582144' class='answer   answerof-408174 ' value='1582144'   \/><label for='answer-id-1582144' id='answer-label-1582144' class=' answer'><span>A SageMaker notebook security group that allows access to Amazon S3<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408174[]' id='answer-id-1582145' class='answer   answerof-408174 ' value='1582145'   \/><label for='answer-id-1582145' id='answer-label-1582145' class=' answer'><span>An 1AM role that allows access to the specific S3 bucket<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408174[]' id='answer-id-1582146' class='answer   answerof-408174 ' value='1582146'   \/><label for='answer-id-1582146' id='answer-label-1582146' class=' answer'><span>A permissive S3 bucket policy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408174[]' id='answer-id-1582147' class='answer   answerof-408174 ' value='1582147'   \/><label for='answer-id-1582147' id='answer-label-1582147' class=' answer'><span>An S3 bucket owner that matches the notebook owner<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408174[]' id='answer-id-1582148' class='answer   answerof-408174 ' value='1582148'   \/><label for='answer-id-1582148' id='answer-label-1582148' class=' answer'><span>A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.<\/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-408175'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours. <br \/>\r<br>The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case. <br \/>\r<br>How should the developer verify the suitability of an ARIMA approach?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='408175' \/><input type='hidden' id='answerType408175' 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-408175[]' id='answer-id-1582149' class='answer   answerof-408175 ' value='1582149'   \/><label for='answer-id-1582149' id='answer-label-1582149' class=' answer'><span>Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408175[]' id='answer-id-1582150' class='answer   answerof-408175 ' value='1582150'   \/><label for='answer-id-1582150' id='answer-label-1582150' class=' answer'><span>Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. \r\nChoose ARIMA as the machine learning (ML) problem. Check the model performance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408175[]' id='answer-id-1582151' class='answer   answerof-408175 ' value='1582151'   \/><label for='answer-id-1582151' id='answer-label-1582151' class=' answer'><span>Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408175[]' id='answer-id-1582152' class='answer   answerof-408175 ' value='1582152'   \/><label for='answer-id-1582152' id='answer-label-1582152' class=' answer'><span>Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.<\/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-408176'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours <br \/>\r<br>With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s). <br \/>\r<br>Which visualization will accomplish this?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='408176' \/><input type='hidden' id='answerType408176' 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-408176[]' id='answer-id-1582153' class='answer   answerof-408176 ' value='1582153'   \/><label for='answer-id-1582153' id='answer-label-1582153' class=' answer'><span>A histogram showing whether the most important input feature is Gaussian.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408176[]' id='answer-id-1582154' class='answer   answerof-408176 ' value='1582154'   \/><label for='answer-id-1582154' id='answer-label-1582154' class=' answer'><span>A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408176[]' id='answer-id-1582155' class='answer   answerof-408176 ' value='1582155'   \/><label for='answer-id-1582155' id='answer-label-1582155' class=' answer'><span>A scatter plot showing (he performance of the objective metric over each training iteration<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408176[]' id='answer-id-1582156' class='answer   answerof-408176 ' value='1582156'   \/><label for='answer-id-1582156' id='answer-label-1582156' class=' answer'><span>A scatter plot showing the correlation between maximum tree depth and the objective metric.<\/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-408177'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data. <br \/>\r<br>The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires. <br \/>\r<br>Which action will MOST likely improve the performance for the forecasting model?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='408177' \/><input type='hidden' id='answerType408177' 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-408177[]' id='answer-id-1582157' class='answer   answerof-408177 ' value='1582157'   \/><label for='answer-id-1582157' id='answer-label-1582157' class=' answer'><span>Aggregate sales from stores in the same geographic area.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408177[]' id='answer-id-1582158' class='answer   answerof-408177 ' value='1582158'   \/><label for='answer-id-1582158' id='answer-label-1582158' class=' answer'><span>Apply smoothing to correct for seasonal variation.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408177[]' id='answer-id-1582159' class='answer   answerof-408177 ' value='1582159'   \/><label for='answer-id-1582159' id='answer-label-1582159' class=' answer'><span>Change the forecast frequency from daily to weekly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408177[]' id='answer-id-1582160' class='answer   answerof-408177 ' value='1582160'   \/><label for='answer-id-1582160' id='answer-label-1582160' class=' answer'><span>Replace missing values in the dataset by using linear interpolation.<\/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-408178'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations. <br \/>\r<br>The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. <br \/>\r<br>The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives. <br \/>\r<br><br><img decoding=\"async\" width=567 height=118 id=\"\u56fe\u7247 14\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/06\/image005-27.jpg\"><br><br \/>\r<br>Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_19' value='408178' \/><input type='hidden' id='answerType408178' 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-408178[]' id='answer-id-1582161' class='answer   answerof-408178 ' value='1582161'   \/><label for='answer-id-1582161' id='answer-label-1582161' class=' answer'><span>Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408178[]' id='answer-id-1582162' class='answer   answerof-408178 ' value='1582162'   \/><label for='answer-id-1582162' id='answer-label-1582162' class=' answer'><span>Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408178[]' id='answer-id-1582163' class='answer   answerof-408178 ' value='1582163'   \/><label for='answer-id-1582163' id='answer-label-1582163' class=' answer'><span>Increase the XGBoost max_depth parameter because the model is currently underfitting the data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408178[]' id='answer-id-1582164' class='answer   answerof-408178 ' value='1582164'   \/><label for='answer-id-1582164' id='answer-label-1582164' class=' answer'><span>Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408178[]' id='answer-id-1582165' class='answer   answerof-408178 ' value='1582165'   \/><label for='answer-id-1582165' id='answer-label-1582165' class=' answer'><span>Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.<\/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-408179'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A company wants to predict the classification of documents that are created from an application. New documents are saved to an Amazon S3 bucket every 3 seconds. The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document. <br \/>\r<br>Which approach will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='408179' \/><input type='hidden' id='answerType408179' 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-408179[]' id='answer-id-1582166' class='answer   answerof-408179 ' value='1582166'   \/><label for='answer-id-1582166' id='answer-label-1582166' class=' answer'><span>Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408179[]' id='answer-id-1582167' class='answer   answerof-408179 ' value='1582167'   \/><label for='answer-id-1582167' id='answer-label-1582167' class=' answer'><span>Deploy all the models to a single SageMaker endpoint. Treat each model as a production variant. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each production variant and return the results of each model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408179[]' id='answer-id-1582168' class='answer   answerof-408179 ' value='1582168'   \/><label for='answer-id-1582168' id='answer-label-1582168' class=' answer'><span>Deploy each model to its own SageMaker endpoint Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each endpoint and return the results of each model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408179[]' id='answer-id-1582169' class='answer   answerof-408179 ' value='1582169'   \/><label for='answer-id-1582169' id='answer-label-1582169' class=' answer'><span>Deploy each model to its own SageMaker endpoint. Create three AWS Lambda functions. \r\nConfigure each Lambda function to call a different endpoint and return the results. Configure three \r\nS3 event notifications to invoke the Lambda functions when new documents are created.<\/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-408180'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen <br \/>\r<br>Which combination of algorithms would provide the appropriate insights? (Select TWO)<\/div><input type='hidden' name='question_id[]' id='qID_21' value='408180' \/><input type='hidden' id='answerType408180' 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-408180[]' id='answer-id-1582170' class='answer   answerof-408180 ' value='1582170'   \/><label for='answer-id-1582170' id='answer-label-1582170' class=' answer'><span>The factorization machines (FM) algorithm<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408180[]' id='answer-id-1582171' class='answer   answerof-408180 ' value='1582171'   \/><label for='answer-id-1582171' id='answer-label-1582171' class=' answer'><span>The Latent Dirichlet Allocation (LDA) algorithm<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408180[]' id='answer-id-1582172' class='answer   answerof-408180 ' value='1582172'   \/><label for='answer-id-1582172' id='answer-label-1582172' class=' answer'><span>The principal component analysis (PCA) algorithm<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408180[]' id='answer-id-1582173' class='answer   answerof-408180 ' value='1582173'   \/><label for='answer-id-1582173' id='answer-label-1582173' class=' answer'><span>The k-means algorithm<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408180[]' id='answer-id-1582174' class='answer   answerof-408180 ' value='1582174'   \/><label for='answer-id-1582174' id='answer-label-1582174' class=' answer'><span>The Random Cut Forest (RCF) algorithm<\/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-408181'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category. Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim\u2019s record in a central database. The claim\u2019s record includes the claim\u2019s category. <br \/>\r<br>The company has no data science team or experience in the field of machine learning (ML). The company\u2019s small development team needs a solution that requires no ML expertise. <br \/>\r<br>Which solution meets these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='408181' \/><input type='hidden' id='answerType408181' 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-408181[]' id='answer-id-1582175' class='answer   answerof-408181 ' value='1582175'   \/><label for='answer-id-1582175' id='answer-label-1582175' class=' answer'><span>Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408181[]' id='answer-id-1582176' class='answer   answerof-408181 ' value='1582176'   \/><label for='answer-id-1582176' id='answer-label-1582176' class=' answer'><span>Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408181[]' id='answer-id-1582177' class='answer   answerof-408181 ' value='1582177'   \/><label for='answer-id-1582177' id='answer-label-1582177' class=' answer'><span>Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408181[]' id='answer-id-1582178' class='answer   answerof-408181 ' value='1582178'   \/><label for='answer-id-1582178' id='answer-label-1582178' class=' answer'><span>Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.<\/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-408182'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that <br \/>\r<br>returns the probability that a given transaction may be fraudulent. <br \/>\r<br>How should the Specialist frame this business problem'?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='408182' \/><input type='hidden' id='answerType408182' 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-408182[]' id='answer-id-1582179' class='answer   answerof-408182 ' value='1582179'   \/><label for='answer-id-1582179' id='answer-label-1582179' class=' answer'><span>Streaming classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408182[]' id='answer-id-1582180' class='answer   answerof-408182 ' value='1582180'   \/><label for='answer-id-1582180' id='answer-label-1582180' class=' answer'><span>Binary classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408182[]' id='answer-id-1582181' class='answer   answerof-408182 ' value='1582181'   \/><label for='answer-id-1582181' id='answer-label-1582181' class=' answer'><span>Multi-category classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408182[]' id='answer-id-1582182' class='answer   answerof-408182 ' value='1582182'   \/><label for='answer-id-1582182' id='answer-label-1582182' class=' answer'><span>Regression classification<\/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-408183'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions. <br \/>\r<br>Which visualization will help the data scientist better understand the data trend?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='408183' \/><input type='hidden' id='answerType408183' 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-408183[]' id='answer-id-1582183' class='answer   answerof-408183 ' value='1582183'   \/><label for='answer-id-1582183' id='answer-label-1582183' class=' answer'><span>Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408183[]' id='answer-id-1582184' class='answer   answerof-408183 ' value='1582184'   \/><label for='answer-id-1582184' id='answer-label-1582184' class=' answer'><span>Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408183[]' id='answer-id-1582185' class='answer   answerof-408183 ' value='1582185'   \/><label for='answer-id-1582185' id='answer-label-1582185' class=' answer'><span>Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408183[]' id='answer-id-1582186' class='answer   answerof-408183 ' value='1582186'   \/><label for='answer-id-1582186' id='answer-label-1582186' class=' answer'><span>Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.<\/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-408184'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target. <br \/>\r<br>What option can the Specialist use to determine whether it is overestimating or underestimating the <br \/>\r<br>target value?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='408184' \/><input type='hidden' id='answerType408184' 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-408184[]' id='answer-id-1582187' class='answer   answerof-408184 ' value='1582187'   \/><label for='answer-id-1582187' id='answer-label-1582187' class=' answer'><span>Root Mean Square Error (RMSE)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408184[]' id='answer-id-1582188' class='answer   answerof-408184 ' value='1582188'   \/><label for='answer-id-1582188' id='answer-label-1582188' class=' answer'><span>Residual plots<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408184[]' id='answer-id-1582189' class='answer   answerof-408184 ' value='1582189'   \/><label for='answer-id-1582189' id='answer-label-1582189' class=' answer'><span>Area under the curve<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408184[]' id='answer-id-1582190' class='answer   answerof-408184 ' value='1582190'   \/><label for='answer-id-1582190' id='answer-label-1582190' class=' answer'><span>Confusion matrix<\/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-408185'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. <br \/>\r<br>Which solution requires the LEAST effort to be able to query this data?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='408185' \/><input type='hidden' id='answerType408185' 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-408185[]' id='answer-id-1582191' class='answer   answerof-408185 ' value='1582191'   \/><label for='answer-id-1582191' id='answer-label-1582191' class=' answer'><span>Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408185[]' id='answer-id-1582192' class='answer   answerof-408185 ' value='1582192'   \/><label for='answer-id-1582192' id='answer-label-1582192' class=' answer'><span>Use AWS Glue to catalogue the data and Amazon Athena to run queries<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408185[]' id='answer-id-1582193' class='answer   answerof-408185 ' value='1582193'   \/><label for='answer-id-1582193' id='answer-label-1582193' class=' answer'><span>Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408185[]' id='answer-id-1582194' class='answer   answerof-408185 ' value='1582194'   \/><label for='answer-id-1582194' id='answer-label-1582194' class=' answer'><span>Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries<\/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-408186'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures. <br \/>\r<br>The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page. <br \/>\r<br>Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='408186' \/><input type='hidden' id='answerType408186' 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-408186[]' id='answer-id-1582195' class='answer   answerof-408186 ' value='1582195'   \/><label for='answer-id-1582195' id='answer-label-1582195' class=' answer'><span>Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURE<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408186[]' id='answer-id-1582196' class='answer   answerof-408186 ' value='1582196'   \/><label for='answer-id-1582196' id='answer-label-1582196' class=' answer'><span>Return the confidence scores for each page.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408186[]' id='answer-id-1582197' class='answer   answerof-408186 ' value='1582197'   \/><label for='answer-id-1582197' id='answer-label-1582197' class=' answer'><span>Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408186[]' id='answer-id-1582198' class='answer   answerof-408186 ' value='1582198'   \/><label for='answer-id-1582198' id='answer-label-1582198' class=' answer'><span>Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408186[]' id='answer-id-1582199' class='answer   answerof-408186 ' value='1582199'   \/><label for='answer-id-1582199' id='answer-label-1582199' class=' answer'><span>Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page<\/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-408187'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features. <br \/>\r<br>Which solution will meet these requirements with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_28' value='408187' \/><input type='hidden' id='answerType408187' 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-408187[]' id='answer-id-1582200' class='answer   answerof-408187 ' value='1582200'   \/><label for='answer-id-1582200' id='answer-label-1582200' class=' answer'><span>Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408187[]' id='answer-id-1582201' class='answer   answerof-408187 ' value='1582201'   \/><label for='answer-id-1582201' id='answer-label-1582201' class=' answer'><span>Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408187[]' id='answer-id-1582202' class='answer   answerof-408187 ' value='1582202'   \/><label for='answer-id-1582202' id='answer-label-1582202' class=' answer'><span>Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408187[]' id='answer-id-1582203' class='answer   answerof-408187 ' value='1582203'   \/><label for='answer-id-1582203' id='answer-label-1582203' class=' answer'><span>Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.<\/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-408188'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages. <br \/>\r<br>The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions The ML spoctafst must ensure that the data does not contain outliers before training the ..el. <br \/>\r<br>What can the ML specialist meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_29' value='408188' \/><input type='hidden' id='answerType408188' 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-408188[]' id='answer-id-1582204' class='answer   answerof-408188 ' value='1582204'   \/><label for='answer-id-1582204' id='answer-label-1582204' class=' answer'><span>Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408188[]' id='answer-id-1582205' class='answer   answerof-408188 ' value='1582205'   \/><label for='answer-id-1582205' id='answer-label-1582205' class=' answer'><span>Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data \r\nWrangler data flow to remove outliers based on the bias report.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408188[]' id='answer-id-1582206' class='answer   answerof-408188 ' value='1582206'   \/><label for='answer-id-1582206' id='answer-label-1582206' class=' answer'><span>Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408188[]' id='answer-id-1582207' class='answer   answerof-408188 ' value='1582207'   \/><label for='answer-id-1582207' id='answer-label-1582207' class=' answer'><span>Use Amazon Lookout for Equipment to find and remove outliers from the dataset.<\/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-408189'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. <br \/>\r<br>Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_30' value='408189' \/><input type='hidden' id='answerType408189' 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-408189[]' id='answer-id-1582208' class='answer   answerof-408189 ' value='1582208'   \/><label for='answer-id-1582208' id='answer-label-1582208' class=' answer'><span>Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408189[]' id='answer-id-1582209' class='answer   answerof-408189 ' value='1582209'   \/><label for='answer-id-1582209' id='answer-label-1582209' class=' answer'><span>Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408189[]' id='answer-id-1582210' class='answer   answerof-408189 ' value='1582210'   \/><label for='answer-id-1582210' id='answer-label-1582210' class=' answer'><span>Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408189[]' id='answer-id-1582211' class='answer   answerof-408189 ' value='1582211'   \/><label for='answer-id-1582211' id='answer-label-1582211' class=' answer'><span>Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408189[]' id='answer-id-1582212' class='answer   answerof-408189 ' value='1582212'   \/><label for='answer-id-1582212' id='answer-label-1582212' class=' answer'><span>Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.<\/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-408190'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A company offers an online shopping service to its customers. The company wants to enhance the site\u2019s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested. The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records <br \/>\r<br>also contain the login name of the requesting user. <br \/>\r<br>Which approach should an ML specialist take to implement the new security feature in the web application?<\/div><input type='hidden' name='question_id[]' id='qID_31' value='408190' \/><input type='hidden' id='answerType408190' 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-408190[]' id='answer-id-1582213' class='answer   answerof-408190 ' value='1582213'   \/><label for='answer-id-1582213' id='answer-label-1582213' class=' answer'><span>Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408190[]' id='answer-id-1582214' class='answer   answerof-408190 ' value='1582214'   \/><label for='answer-id-1582214' id='answer-label-1582214' class=' answer'><span>Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408190[]' id='answer-id-1582215' class='answer   answerof-408190 ' value='1582215'   \/><label for='answer-id-1582215' id='answer-label-1582215' class=' answer'><span>Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408190[]' id='answer-id-1582216' class='answer   answerof-408190 ' value='1582216'   \/><label for='answer-id-1582216' id='answer-label-1582216' class=' answer'><span>Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.<\/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-408191'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced. <br \/>\r<br>What should the engineer do to improve the validation accuracy of the model?<\/div><input type='hidden' name='question_id[]' id='qID_32' value='408191' \/><input type='hidden' id='answerType408191' 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-408191[]' id='answer-id-1582217' class='answer   answerof-408191 ' value='1582217'   \/><label for='answer-id-1582217' id='answer-label-1582217' class=' answer'><span>Perform stratified sampling on the original dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408191[]' id='answer-id-1582218' class='answer   answerof-408191 ' value='1582218'   \/><label for='answer-id-1582218' id='answer-label-1582218' class=' answer'><span>Acquire additional data about the majority classes in the original dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408191[]' id='answer-id-1582219' class='answer   answerof-408191 ' value='1582219'   \/><label for='answer-id-1582219' id='answer-label-1582219' class=' answer'><span>Use a smaller, randomly sampled version of the training dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408191[]' id='answer-id-1582220' class='answer   answerof-408191 ' value='1582220'   \/><label for='answer-id-1582220' id='answer-label-1582220' class=' answer'><span>Perform systematic sampling on the original dataset.<\/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-408192'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>A company is using a machine learning (ML) model to recommend products to customers. An ML specialist wants to analyze the data for the most popular recommendations in four dimensions. The ML specialist will visualize the first two dimensions as coordinates. The third dimension will be visualized as color. The ML specialist will use size to represent the fourth dimension in the visualization. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='408192' \/><input type='hidden' id='answerType408192' 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-408192[]' id='answer-id-1582221' class='answer   answerof-408192 ' value='1582221'   \/><label for='answer-id-1582221' id='answer-label-1582221' class=' answer'><span>Use the Amazon SageMaker Data Wrangler bar chart feature. Use Group By to represent the third and fourth dimensions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408192[]' id='answer-id-1582222' class='answer   answerof-408192 ' value='1582222'   \/><label for='answer-id-1582222' id='answer-label-1582222' class=' answer'><span>Use the Amazon SageMaker Canvas box plot visualization. Use color and fill pattern to represent the third and fourth dimensions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408192[]' id='answer-id-1582223' class='answer   answerof-408192 ' value='1582223'   \/><label for='answer-id-1582223' id='answer-label-1582223' class=' answer'><span>Use the Amazon SageMaker Data Wrangler histogram feature. Use color and fill pattern to represent the third and fourth dimensions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408192[]' id='answer-id-1582224' class='answer   answerof-408192 ' value='1582224'   \/><label for='answer-id-1582224' id='answer-label-1582224' class=' answer'><span>Use the Amazon SageMaker Canvas scatter plot visualization. Use scatter point size and color to represent the third and fourth dimensions.<\/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-408193'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users. <br \/>\r<br>The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company\u2019s business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models. <br \/>\r<br>Which solution satisfies these requirements with MINIMAL effort?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='408193' \/><input type='hidden' id='answerType408193' 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-408193[]' id='answer-id-1582225' class='answer   answerof-408193 ' value='1582225'   \/><label for='answer-id-1582225' id='answer-label-1582225' class=' answer'><span>Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408193[]' id='answer-id-1582226' class='answer   answerof-408193 ' value='1582226'   \/><label for='answer-id-1582226' id='answer-label-1582226' class=' answer'><span>Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408193[]' id='answer-id-1582227' class='answer   answerof-408193 ' value='1582227'   \/><label for='answer-id-1582227' id='answer-label-1582227' class=' answer'><span>Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408193[]' id='answer-id-1582228' class='answer   answerof-408193 ' value='1582228'   \/><label for='answer-id-1582228' id='answer-label-1582228' class=' answer'><span>Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.<\/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-408194'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs <br \/>\r<br>What does the Specialist need to do1?<\/div><input type='hidden' name='question_id[]' id='qID_35' value='408194' \/><input type='hidden' id='answerType408194' 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-408194[]' id='answer-id-1582229' class='answer   answerof-408194 ' value='1582229'   \/><label for='answer-id-1582229' id='answer-label-1582229' class=' answer'><span>Bundle the NVIDIA drivers with the Docker image<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408194[]' id='answer-id-1582230' class='answer   answerof-408194 ' value='1582230'   \/><label for='answer-id-1582230' id='answer-label-1582230' class=' answer'><span>Build the Docker container to be NVIDIA-Docker compatible<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408194[]' id='answer-id-1582231' class='answer   answerof-408194 ' value='1582231'   \/><label for='answer-id-1582231' id='answer-label-1582231' class=' answer'><span>Organize the Docker container's file structure to execute on GPU instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408194[]' id='answer-id-1582232' class='answer   answerof-408194 ' value='1582232'   \/><label for='answer-id-1582232' id='answer-label-1582232' class=' answer'><span>Set the GPU flag in the Amazon SageMaker Create TrainingJob request body<\/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-408195'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist. <br \/>\r<br>Which machine learning model type should the Specialist use to accomplish this task?<\/div><input type='hidden' name='question_id[]' id='qID_36' value='408195' \/><input type='hidden' id='answerType408195' 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-408195[]' id='answer-id-1582233' class='answer   answerof-408195 ' value='1582233'   \/><label for='answer-id-1582233' id='answer-label-1582233' class=' answer'><span>Linear regression<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408195[]' id='answer-id-1582234' class='answer   answerof-408195 ' value='1582234'   \/><label for='answer-id-1582234' id='answer-label-1582234' class=' answer'><span>Classification<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408195[]' id='answer-id-1582235' class='answer   answerof-408195 ' value='1582235'   \/><label for='answer-id-1582235' id='answer-label-1582235' class=' answer'><span>Clustering<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408195[]' id='answer-id-1582236' class='answer   answerof-408195 ' value='1582236'   \/><label for='answer-id-1582236' id='answer-label-1582236' class=' answer'><span>Reinforcement learning<\/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-408196'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions - Here is an example from the dataset <br \/>\r<br>&quot;The quck BROWN FOX jumps over the lazy dog &quot; <br \/>\r<br>Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)<\/div><input type='hidden' name='question_id[]' id='qID_37' value='408196' \/><input type='hidden' id='answerType408196' 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-408196[]' id='answer-id-1582237' class='answer   answerof-408196 ' value='1582237'   \/><label for='answer-id-1582237' id='answer-label-1582237' class=' answer'><span>Perform part-of-speech tagging and keep the action verb and the nouns only<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408196[]' id='answer-id-1582238' class='answer   answerof-408196 ' value='1582238'   \/><label for='answer-id-1582238' id='answer-label-1582238' class=' answer'><span>Normalize all words by making the sentence lowercase<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408196[]' id='answer-id-1582239' class='answer   answerof-408196 ' value='1582239'   \/><label for='answer-id-1582239' id='answer-label-1582239' class=' answer'><span>Remove stop words using an English stopword dictionary.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408196[]' id='answer-id-1582240' class='answer   answerof-408196 ' value='1582240'   \/><label for='answer-id-1582240' id='answer-label-1582240' class=' answer'><span>Correct the typography on &quot;quck&quot; to &quot;quick.&quot;<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408196[]' id='answer-id-1582241' class='answer   answerof-408196 ' value='1582241'   \/><label for='answer-id-1582241' id='answer-label-1582241' class=' answer'><span>One-hot encode all words in the sentence<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408196[]' id='answer-id-1582242' class='answer   answerof-408196 ' value='1582242'   \/><label for='answer-id-1582242' id='answer-label-1582242' class=' answer'><span>Tokenize the sentence into words.<\/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-408197'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_38' value='408197' \/><input type='hidden' id='answerType408197' 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-408197[]' id='answer-id-1582243' class='answer   answerof-408197 ' value='1582243'   \/><label for='answer-id-1582243' id='answer-label-1582243' class=' answer'><span>Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408197[]' id='answer-id-1582244' class='answer   answerof-408197 ' value='1582244'   \/><label for='answer-id-1582244' id='answer-label-1582244' class=' answer'><span>Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408197[]' id='answer-id-1582245' class='answer   answerof-408197 ' value='1582245'   \/><label for='answer-id-1582245' id='answer-label-1582245' class=' answer'><span>Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408197[]' id='answer-id-1582246' class='answer   answerof-408197 ' value='1582246'   \/><label for='answer-id-1582246' id='answer-label-1582246' class=' answer'><span>Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.<\/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-408198'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 <br \/>\r<br>The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 <br \/>\r<br>Which approach will provide the information required for further analysis?<\/div><input type='hidden' name='question_id[]' id='qID_39' value='408198' \/><input type='hidden' id='answerType408198' 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-408198[]' id='answer-id-1582247' class='answer   answerof-408198 ' value='1582247'   \/><label for='answer-id-1582247' id='answer-label-1582247' class=' answer'><span>Use Amazon Comprehend with the transcribed files to build the key topics<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408198[]' id='answer-id-1582248' class='answer   answerof-408198 ' value='1582248'   \/><label for='answer-id-1582248' id='answer-label-1582248' class=' answer'><span>Use Amazon Translate with the transcribed files to train and build a model for the key topics<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408198[]' id='answer-id-1582249' class='answer   answerof-408198 ' value='1582249'   \/><label for='answer-id-1582249' id='answer-label-1582249' class=' answer'><span>Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408198[]' id='answer-id-1582250' class='answer   answerof-408198 ' value='1582250'   \/><label for='answer-id-1582250' id='answer-label-1582250' class=' answer'><span>Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics<\/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-408199'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>A Machine Learning Specialist discover the following statistics while experimenting on a model. <br \/>\r<br>What can the Specialist from the experiments?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='408199' \/><input type='hidden' id='answerType408199' 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-408199[]' id='answer-id-1582251' class='answer   answerof-408199 ' value='1582251'   \/><label for='answer-id-1582251' id='answer-label-1582251' class=' answer'><span>The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408199[]' id='answer-id-1582252' class='answer   answerof-408199 ' value='1582252'   \/><label for='answer-id-1582252' id='answer-label-1582252' class=' answer'><span>The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408199[]' id='answer-id-1582253' class='answer   answerof-408199 ' value='1582253'   \/><label for='answer-id-1582253' id='answer-label-1582253' class=' answer'><span>The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408199[]' id='answer-id-1582254' class='answer   answerof-408199 ' value='1582254'   \/><label for='answer-id-1582254' id='answer-label-1582254' class=' answer'><span>The model in Experiment 1 had a high random noise error that was reduced in Experiment 3 by regularization Experiment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model<\/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=\"watuPROButtons10290\" >\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)\" 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408195:1582233,1582234,1582235,1582236 | 408196:1582237,1582238,1582239,1582240,1582241,1582242 | 408197:1582243,1582244,1582245,1582246 | 408198:1582247,1582248,1582249,1582250 | 408199:1582251,1582252,1582253,1582254\" \/>\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 = \"408160,408161,408162,408163,408164,408165,408166,408167,408168,408169,408170,408171,408172,408173,408174,408175,408176,408177,408178,408179,408180,408181,408182,408183,408184,408185,408186,408187,408188,408189,408190,408191,408192,408193,408194,408195,408196,408197,408198,408199\";\nWatuPROSettings[10290] = {};\nWatuPRO.qArr = question_ids.split(',');\nWatuPRO.exam_id = 10290;\t    \nWatuPRO.post_id = 113876;\nWatuPRO.store_progress = 0;\nWatuPRO.curCatPage = 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