{"id":113345,"date":"2025-11-03T08:23:27","date_gmt":"2025-11-03T08:23:27","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=113345"},"modified":"2025-11-12T05:56:15","modified_gmt":"2025-11-12T05:56:15","slug":"continue-to-read-mls-c01-free-dumps-part-2-q41-q80-today-trust-the-mls-c01-dumps-v13-02-are-reliable-for-preparation","status":"publish","type":"post","link":"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","title":{"rendered":"Continue to Read MLS-C01 Free Dumps (Part 2, Q41-Q80) Today: Trust the MLS-C01 Dumps (V13.02) Are Reliable for Preparation"},"content":{"rendered":"<p>You can trust that the MLS-C01 dumps (V13.02) are reliable for your AWS Certified Machine Learning &#8211; Specialty exam preparation, enabling you to develop effective strategies for answering questions accurately and efficiently. We have shared the <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\"><strong><em>MLS-C01 free dumps (Part 1, Q1-Q40) of V13.02<\/em><\/strong><\/a> online, and you can check first to verify the quality. By using current dumps, you can access the most recent MLS-C01 exam questions, ensuring that your preparation aligns with the latest exam expectations. Using the MLS-C01 dumps (V13.02) helps you avoid surprises on exam day and enhances your ability to respond to questions effectively. Come here and continue to read our free dumps today. We are sharing more to help you check the quality again.<\/p>\n<h2>Below are our AWS <span style=\"background-color: #ffff99;\"><em>MLS-C01 free dumps (Part 2, Q41-Q80) of V13.02<\/em><\/span> for checking more:<\/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=\"submittingExam10289\" 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-10289\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-10289\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-408120'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values. <br \/>\r<br>Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling. <br \/>\r<br>Which solution will meet these requirements with the LEAST implementation effort?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='408120' \/><input type='hidden' id='answerType408120' 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-408120[]' id='answer-id-1581913' class='answer   answerof-408120 ' value='1581913'   \/><label for='answer-id-1581913' id='answer-label-1581913' class=' answer'><span>Use Amazon EMR Serveriess with PySpark.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408120[]' id='answer-id-1581914' class='answer   answerof-408120 ' value='1581914'   \/><label for='answer-id-1581914' id='answer-label-1581914' class=' answer'><span>Use AWS Glue DataBrew.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408120[]' id='answer-id-1581915' class='answer   answerof-408120 ' value='1581915'   \/><label for='answer-id-1581915' id='answer-label-1581915' class=' answer'><span>Use Amazon SageMaker Studio Data Wrangler.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408120[]' id='answer-id-1581916' class='answer   answerof-408120 ' value='1581916'   \/><label for='answer-id-1581916' id='answer-label-1581916' class=' answer'><span>Use Amazon SageMaker Studio Notebook with Pandas.<\/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-408121'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website. <br \/>\r<br>Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone. <br \/>\r<br>Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_2' value='408121' \/><input type='hidden' id='answerType408121' 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-408121[]' id='answer-id-1581917' class='answer   answerof-408121 ' value='1581917'   \/><label for='answer-id-1581917' id='answer-label-1581917' class=' answer'><span>Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408121[]' id='answer-id-1581918' class='answer   answerof-408121 ' value='1581918'   \/><label for='answer-id-1581918' id='answer-label-1581918' class=' answer'><span>Create a new endpoint configuration with two production variants.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408121[]' id='answer-id-1581919' class='answer   answerof-408121 ' value='1581919'   \/><label for='answer-id-1581919' id='answer-label-1581919' class=' answer'><span>Configure the endpoint to automatically scale with the Invocations Per Instance metric.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408121[]' id='answer-id-1581920' class='answer   answerof-408121 ' value='1581920'   \/><label for='answer-id-1581920' id='answer-label-1581920' class=' answer'><span>Deploy a second instance pool to support a blue\/green deployment of models.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408121[]' id='answer-id-1581921' class='answer   answerof-408121 ' value='1581921'   \/><label for='answer-id-1581921' id='answer-label-1581921' class=' answer'><span>Reconfigure the endpoint to use burstable instances.<\/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-408122'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset. <br \/>\r<br>Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)<\/div><input type='hidden' name='question_id[]' id='qID_3' value='408122' \/><input type='hidden' id='answerType408122' 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-408122[]' id='answer-id-1581922' class='answer   answerof-408122 ' value='1581922'   \/><label for='answer-id-1581922' id='answer-label-1581922' class=' answer'><span>Emails exchanged by customers and the company\u2019s customer service agents<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408122[]' id='answer-id-1581923' class='answer   answerof-408122 ' value='1581923'   \/><label for='answer-id-1581923' id='answer-label-1581923' class=' answer'><span>Social media posts containing the name of the company or its products<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408122[]' id='answer-id-1581924' class='answer   answerof-408122 ' value='1581924'   \/><label for='answer-id-1581924' id='answer-label-1581924' class=' answer'><span>A publicly available collection of news articles<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408122[]' id='answer-id-1581925' class='answer   answerof-408122 ' value='1581925'   \/><label for='answer-id-1581925' id='answer-label-1581925' class=' answer'><span>A publicly available collection of customer reviews<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408122[]' id='answer-id-1581926' class='answer   answerof-408122 ' value='1581926'   \/><label for='answer-id-1581926' id='answer-label-1581926' class=' answer'><span>Product sales revenue figures for the company<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408122[]' id='answer-id-1581927' class='answer   answerof-408122 ' value='1581927'   \/><label for='answer-id-1581927' id='answer-label-1581927' class=' answer'><span>Instruction manuals for the company\u2019s products<\/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-408123'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made. <br \/>\r<br>The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days. <br \/>\r<br>The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science. <br \/>\r<br>What should the data science team do to address this issue in the MOST operationally efficient manner?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='408123' \/><input type='hidden' id='answerType408123' 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-408123[]' id='answer-id-1581928' class='answer   answerof-408123 ' value='1581928'   \/><label for='answer-id-1581928' id='answer-label-1581928' class=' answer'><span>Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Enable Amazon SageMaker Model Monitor to store inferences. Use the inferences to create Shapley values that help explain model behavior. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408123[]' id='answer-id-1581929' class='answer   answerof-408123 ' value='1581929'   \/><label for='answer-id-1581929' id='answer-label-1581929' class=' answer'><span>Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Activate Amazon SageMaker Debugger, and configure it to calculate and collect Shapley values. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408123[]' id='answer-id-1581930' class='answer   answerof-408123 ' value='1581930'   \/><label for='answer-id-1581930' id='answer-label-1581930' class=' answer'><span>Create an Amazon SageMaker notebook instance. Use the notebook instance and the XGBoost library to locally retrain the model. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart. Use that chart to explain to the credit team how the features affect the model outcomes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408123[]' id='answer-id-1581931' class='answer   answerof-408123 ' value='1581931'   \/><label for='answer-id-1581931' id='answer-label-1581931' class=' answer'><span>Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Use Amazon SageMaker Processing to post-analyze the model and create a feature importance explainability chart automatically for the credit team.<\/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-408124'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy. <br \/>\r<br>What is the MOST effective way to encode this categorical feature into a numeric feature?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='408124' \/><input type='hidden' id='answerType408124' 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-408124[]' id='answer-id-1581932' class='answer   answerof-408124 ' value='1581932'   \/><label for='answer-id-1581932' id='answer-label-1581932' class=' answer'><span>Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408124[]' id='answer-id-1581933' class='answer   answerof-408124 ' value='1581933'   \/><label for='answer-id-1581933' id='answer-label-1581933' class=' answer'><span>Fix the spelling in the column by using char-RN<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408124[]' id='answer-id-1581934' class='answer   answerof-408124 ' value='1581934'   \/><label for='answer-id-1581934' id='answer-label-1581934' class=' answer'><span>Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408124[]' id='answer-id-1581935' class='answer   answerof-408124 ' value='1581935'   \/><label for='answer-id-1581935' id='answer-label-1581935' class=' answer'><span>Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408124[]' id='answer-id-1581936' class='answer   answerof-408124 ' value='1581936'   \/><label for='answer-id-1581936' id='answer-label-1581936' class=' answer'><span>Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.<\/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-408125'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution. <br \/>\r<br>After training, the model's inferences accuracy is lower than expected. <br \/>\r<br>Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='408125' \/><input type='hidden' id='answerType408125' 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-408125[]' id='answer-id-1581937' class='answer   answerof-408125 ' value='1581937'   \/><label for='answer-id-1581937' id='answer-label-1581937' class=' answer'><span>Normalize the problematic features.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408125[]' id='answer-id-1581938' class='answer   answerof-408125 ' value='1581938'   \/><label for='answer-id-1581938' id='answer-label-1581938' class=' answer'><span>Bootstrap the problematic features.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408125[]' id='answer-id-1581939' class='answer   answerof-408125 ' value='1581939'   \/><label for='answer-id-1581939' id='answer-label-1581939' class=' answer'><span>Remove the problematic features.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408125[]' id='answer-id-1581940' class='answer   answerof-408125 ' value='1581940'   \/><label for='answer-id-1581940' id='answer-label-1581940' class=' answer'><span>Extrapolate synthetic features.<\/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-408126'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing. <br \/>\r<br>How it should the data science team do to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='408126' \/><input type='hidden' id='answerType408126' 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-408126[]' id='answer-id-1581941' class='answer   answerof-408126 ' value='1581941'   \/><label for='answer-id-1581941' id='answer-label-1581941' class=' answer'><span>Create new features and interaction variables.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408126[]' id='answer-id-1581942' class='answer   answerof-408126 ' value='1581942'   \/><label for='answer-id-1581942' id='answer-label-1581942' class=' answer'><span>Use a principal component analysis (PCA) model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408126[]' id='answer-id-1581943' class='answer   answerof-408126 ' value='1581943'   \/><label for='answer-id-1581943' id='answer-label-1581943' class=' answer'><span>Apply normalization on the feature set.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408126[]' id='answer-id-1581944' class='answer   answerof-408126 ' value='1581944'   \/><label for='answer-id-1581944' id='answer-label-1581944' class=' answer'><span>Use a multiple correspondence analysis (MCA) model<\/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-408127'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A manufacturing company stores production volume data in a PostgreSQL database. <br \/>\r<br>The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge. <br \/>\r<br>Which solution will meet these requirements with the LEAST effort?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='408127' \/><input type='hidden' id='answerType408127' 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-408127[]' id='answer-id-1581945' class='answer   answerof-408127 ' value='1581945'   \/><label for='answer-id-1581945' id='answer-label-1581945' class=' answer'><span>Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR cluster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408127[]' id='answer-id-1581946' class='answer   answerof-408127 ' value='1581946'   \/><label for='answer-id-1581946' id='answer-label-1581946' class=' answer'><span>Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408127[]' id='answer-id-1581947' class='answer   answerof-408127 ' value='1581947'   \/><label for='answer-id-1581947' id='answer-label-1581947' class=' answer'><span>Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408127[]' id='answer-id-1581948' class='answer   answerof-408127 ' value='1581948'   \/><label for='answer-id-1581948' id='answer-label-1581948' class=' answer'><span>Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.<\/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-408128'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training. <br \/>\r<br>How should a data scientist adjust the solution?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='408128' \/><input type='hidden' id='answerType408128' 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-408128[]' id='answer-id-1581949' class='answer   answerof-408128 ' value='1581949'   \/><label for='answer-id-1581949' id='answer-label-1581949' class=' answer'><span>Use the event tracker in Amazon Personalize to include real-time user interactions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408128[]' id='answer-id-1581950' class='answer   answerof-408128 ' value='1581950'   \/><label for='answer-id-1581950' id='answer-label-1581950' class=' answer'><span>Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408128[]' id='answer-id-1581951' class='answer   answerof-408128 ' value='1581951'   \/><label for='answer-id-1581951' id='answer-label-1581951' class=' answer'><span>Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408128[]' id='answer-id-1581952' class='answer   answerof-408128 ' value='1581952'   \/><label for='answer-id-1581952' id='answer-label-1581952' class=' answer'><span>Add event type and event value fields to the interactions dataset in Amazon Personalize.<\/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-408129'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining. <br \/>\r<br>How should a machine learning specialist meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='408129' \/><input type='hidden' id='answerType408129' 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-408129[]' id='answer-id-1581953' class='answer   answerof-408129 ' value='1581953'   \/><label for='answer-id-1581953' id='answer-label-1581953' class=' answer'><span>Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408129[]' id='answer-id-1581954' class='answer   answerof-408129 ' value='1581954'   \/><label for='answer-id-1581954' id='answer-label-1581954' class=' answer'><span>Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408129[]' id='answer-id-1581955' class='answer   answerof-408129 ' value='1581955'   \/><label for='answer-id-1581955' id='answer-label-1581955' class=' answer'><span>Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408129[]' id='answer-id-1581956' class='answer   answerof-408129 ' value='1581956'   \/><label for='answer-id-1581956' id='answer-label-1581956' class=' answer'><span>Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.<\/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-408130'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget. <br \/>\r<br>What should the Specialist do to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='408130' \/><input type='hidden' id='answerType408130' 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-408130[]' id='answer-id-1581957' class='answer   answerof-408130 ' value='1581957'   \/><label for='answer-id-1581957' id='answer-label-1581957' class=' answer'><span>Create one-hot word encoding vectors.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408130[]' id='answer-id-1581958' class='answer   answerof-408130 ' value='1581958'   \/><label for='answer-id-1581958' id='answer-label-1581958' class=' answer'><span>Produce a set of synonyms for every word using Amazon Mechanical Turk.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408130[]' id='answer-id-1581959' class='answer   answerof-408130 ' value='1581959'   \/><label for='answer-id-1581959' id='answer-label-1581959' class=' answer'><span>Create word embedding factors that store edit distance with every other word.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408130[]' id='answer-id-1581960' class='answer   answerof-408130 ' value='1581960'   \/><label for='answer-id-1581960' id='answer-label-1581960' class=' answer'><span>Download word embedding\u2019s pre-trained on a large corpus.<\/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-408131'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3. <br \/>\r<br>The company wants to use a machine learning (ML) approach to detect fraud in the transformed data. <br \/>\r<br>Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)<\/div><input type='hidden' name='question_id[]' id='qID_12' value='408131' \/><input type='hidden' id='answerType408131' 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-408131[]' id='answer-id-1581961' class='answer   answerof-408131 ' value='1581961'   \/><label for='answer-id-1581961' id='answer-label-1581961' class=' answer'><span>Use Amazon Athena to scan the data and identify the schema.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408131[]' id='answer-id-1581962' class='answer   answerof-408131 ' value='1581962'   \/><label for='answer-id-1581962' id='answer-label-1581962' class=' answer'><span>Use AWS Glue crawlers to scan the data and identify the schema.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408131[]' id='answer-id-1581963' class='answer   answerof-408131 ' value='1581963'   \/><label for='answer-id-1581963' id='answer-label-1581963' class=' answer'><span>Use Amazon Redshift to store procedures to perform data transformations<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408131[]' id='answer-id-1581964' class='answer   answerof-408131 ' value='1581964'   \/><label for='answer-id-1581964' id='answer-label-1581964' class=' answer'><span>Use AWS Glue workflows and AWS Glue jobs to perform data transformations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408131[]' id='answer-id-1581965' class='answer   answerof-408131 ' value='1581965'   \/><label for='answer-id-1581965' id='answer-label-1581965' class=' answer'><span>Use Amazon Redshift ML to train a model to detect fraud.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408131[]' id='answer-id-1581966' class='answer   answerof-408131 ' value='1581966'   \/><label for='answer-id-1581966' id='answer-label-1581966' class=' answer'><span>Use Amazon Fraud Detector to train a model to detect fraud.<\/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-408132'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies. <br \/>\r<br>Which model should be used for categorizing new products using the provided dataset for training?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='408132' \/><input type='hidden' id='answerType408132' 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-408132[]' id='answer-id-1581967' class='answer   answerof-408132 ' value='1581967'   \/><label for='answer-id-1581967' id='answer-label-1581967' class=' answer'><span>An XGBoost model where the objective parameter is set to multi: softmax<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408132[]' id='answer-id-1581968' class='answer   answerof-408132 ' value='1581968'   \/><label for='answer-id-1581968' id='answer-label-1581968' class=' answer'><span>A deep convolutional neural network (CNN) with a softmax activation function for the last layer<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408132[]' id='answer-id-1581969' class='answer   answerof-408132 ' value='1581969'   \/><label for='answer-id-1581969' id='answer-label-1581969' class=' answer'><span>A regression forest where the number of trees is set equal to the number of product categories<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408132[]' id='answer-id-1581970' class='answer   answerof-408132 ' value='1581970'   \/><label for='answer-id-1581970' id='answer-label-1581970' class=' answer'><span>A DeepAR forecasting model based on a recurrent neural network (RNN)<\/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-408133'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A company is using Amazon Polly to translate plaintext documents to speech for automated <br \/>\r<br>company announcements However company acronyms are being mispronounced in the current documents. <br \/>\r<br>How should a Machine Learning Specialist address this issue for future documents?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='408133' \/><input type='hidden' id='answerType408133' 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-408133[]' id='answer-id-1581971' class='answer   answerof-408133 ' value='1581971'   \/><label for='answer-id-1581971' id='answer-label-1581971' class=' answer'><span>Convert current documents to SSML with pronunciation tags<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408133[]' id='answer-id-1581972' class='answer   answerof-408133 ' value='1581972'   \/><label for='answer-id-1581972' id='answer-label-1581972' class=' answer'><span>Create an appropriate pronunciation lexicon.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408133[]' id='answer-id-1581973' class='answer   answerof-408133 ' value='1581973'   \/><label for='answer-id-1581973' id='answer-label-1581973' class=' answer'><span>Output speech marks to guide in pronunciation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408133[]' id='answer-id-1581974' class='answer   answerof-408133 ' value='1581974'   \/><label for='answer-id-1581974' id='answer-label-1581974' class=' answer'><span>Use Amazon Lex to preprocess the text files for pronunciation<\/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-408134'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company\u2019s data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters. <br \/>\r<br>The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='408134' \/><input type='hidden' id='answerType408134' 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-408134[]' id='answer-id-1581975' class='answer   answerof-408134 ' value='1581975'   \/><label for='answer-id-1581975' id='answer-label-1581975' class=' answer'><span>Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408134[]' id='answer-id-1581976' class='answer   answerof-408134 ' value='1581976'   \/><label for='answer-id-1581976' id='answer-label-1581976' class=' answer'><span>Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408134[]' id='answer-id-1581977' class='answer   answerof-408134 ' value='1581977'   \/><label for='answer-id-1581977' id='answer-label-1581977' class=' answer'><span>Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408134[]' id='answer-id-1581978' class='answer   answerof-408134 ' value='1581978'   \/><label for='answer-id-1581978' id='answer-label-1581978' class=' answer'><span>Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.<\/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-408135'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A machine learning (ML) specialist is running an Amazon SageMaker hyperparameter optimization job for a model that is based on the XGBoost algorithm. The ML specialist selects Root Mean Square Error (RMSE) as the objective evaluation metric. <br \/>\r<br>The ML specialist discovers that the model is overfitting and cannot generalize well on the validation data. The ML specialist decides to resolve the model overfitting by using SageMaker automatic model tuning (AMT). <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='408135' \/><input type='hidden' id='answerType408135' 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-408135[]' id='answer-id-1581979' class='answer   answerof-408135 ' value='1581979'   \/><label for='answer-id-1581979' id='answer-label-1581979' class=' answer'><span>Configure SageMaker AMT to use a static range of hyperparameter values.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408135[]' id='answer-id-1581980' class='answer   answerof-408135 ' value='1581980'   \/><label for='answer-id-1581980' id='answer-label-1581980' class=' answer'><span>Configure SageMaker AMT to increase the number of parallel training jobs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408135[]' id='answer-id-1581981' class='answer   answerof-408135 ' value='1581981'   \/><label for='answer-id-1581981' id='answer-label-1581981' class=' answer'><span>Configure SageMaker AMT to stop training jobs early.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408135[]' id='answer-id-1581982' class='answer   answerof-408135 ' value='1581982'   \/><label for='answer-id-1581982' id='answer-label-1581982' class=' answer'><span>Configure SageMaker AMT to run the training jobs with a warm start.<\/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-408136'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>Which of the following metrics should a Machine Learning Specialist generally use to compare\/evaluate machine learning classification models against each other?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='408136' \/><input type='hidden' id='answerType408136' 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-408136[]' id='answer-id-1581983' class='answer   answerof-408136 ' value='1581983'   \/><label for='answer-id-1581983' id='answer-label-1581983' class=' answer'><span>Recall<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408136[]' id='answer-id-1581984' class='answer   answerof-408136 ' value='1581984'   \/><label for='answer-id-1581984' id='answer-label-1581984' class=' answer'><span>Misclassification rate<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408136[]' id='answer-id-1581985' class='answer   answerof-408136 ' value='1581985'   \/><label for='answer-id-1581985' id='answer-label-1581985' class=' answer'><span>Mean absolute percentage error (MAPE)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408136[]' id='answer-id-1581986' class='answer   answerof-408136 ' value='1581986'   \/><label for='answer-id-1581986' id='answer-label-1581986' class=' answer'><span>Area Under the ROC Curve (AUC)<\/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-408137'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet. <br \/>\r<br>How should the company set up the job?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='408137' \/><input type='hidden' id='answerType408137' 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-408137[]' id='answer-id-1581987' class='answer   answerof-408137 ' value='1581987'   \/><label for='answer-id-1581987' id='answer-label-1581987' class=' answer'><span>Launch the notebook instances in a public subnet and access the data through the public S3 endpoint<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408137[]' id='answer-id-1581988' class='answer   answerof-408137 ' value='1581988'   \/><label for='answer-id-1581988' id='answer-label-1581988' class=' answer'><span>Launch the notebook instances in a private subnet and access the data through a NAT gateway<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408137[]' id='answer-id-1581989' class='answer   answerof-408137 ' value='1581989'   \/><label for='answer-id-1581989' id='answer-label-1581989' class=' answer'><span>Launch the notebook instances in a public subnet and access the data through a NAT gateway<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408137[]' id='answer-id-1581990' class='answer   answerof-408137 ' value='1581990'   \/><label for='answer-id-1581990' id='answer-label-1581990' class=' answer'><span>Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.<\/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-408138'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank. <br \/>\r<br>A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='408138' \/><input type='hidden' id='answerType408138' 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-408138[]' id='answer-id-1581991' class='answer   answerof-408138 ' value='1581991'   \/><label for='answer-id-1581991' id='answer-label-1581991' class=' answer'><span>Apply anomaly detection to remove outliers from the training dataset before training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408138[]' id='answer-id-1581992' class='answer   answerof-408138 ' value='1581992'   \/><label for='answer-id-1581992' id='answer-label-1581992' class=' answer'><span>Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408138[]' id='answer-id-1581993' class='answer   answerof-408138 ' value='1581993'   \/><label for='answer-id-1581993' id='answer-label-1581993' class=' answer'><span>Apply normalization to the features of the training dataset before training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408138[]' id='answer-id-1581994' class='answer   answerof-408138 ' value='1581994'   \/><label for='answer-id-1581994' id='answer-label-1581994' class=' answer'><span>Apply undersampling to the training dataset before training.<\/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-408139'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal. <br \/>\r<br>What should the data scientist do to identify and address training issues with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='408139' \/><input type='hidden' id='answerType408139' 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-408139[]' id='answer-id-1581995' class='answer   answerof-408139 ' value='1581995'   \/><label for='answer-id-1581995' id='answer-label-1581995' class=' answer'><span>Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408139[]' id='answer-id-1581996' class='answer   answerof-408139 ' value='1581996'   \/><label for='answer-id-1581996' id='answer-label-1581996' class=' answer'><span>Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408139[]' id='answer-id-1581997' class='answer   answerof-408139 ' value='1581997'   \/><label for='answer-id-1581997' id='answer-label-1581997' class=' answer'><span>Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408139[]' id='answer-id-1581998' class='answer   answerof-408139 ' value='1581998'   \/><label for='answer-id-1581998' id='answer-label-1581998' class=' answer'><span>Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to \r\ndetect issues and to launch the StopTrainingJob action if issues are detected.<\/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-408140'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million <br \/>\r<br>observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce. <br \/>\r<br>Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_21' value='408140' \/><input type='hidden' id='answerType408140' 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-408140[]' id='answer-id-1581999' class='answer   answerof-408140 ' value='1581999'   \/><label for='answer-id-1581999' id='answer-label-1581999' class=' answer'><span>Cross-validation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408140[]' id='answer-id-1582000' class='answer   answerof-408140 ' value='1582000'   \/><label for='answer-id-1582000' id='answer-label-1582000' class=' answer'><span>Numerical value binning<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408140[]' id='answer-id-1582001' class='answer   answerof-408140 ' value='1582001'   \/><label for='answer-id-1582001' id='answer-label-1582001' class=' answer'><span>High-degree polynomial transformation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408140[]' id='answer-id-1582002' class='answer   answerof-408140 ' value='1582002'   \/><label for='answer-id-1582002' id='answer-label-1582002' class=' answer'><span>Logarithmic transformation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408140[]' id='answer-id-1582003' class='answer   answerof-408140 ' value='1582003'   \/><label for='answer-id-1582003' id='answer-label-1582003' class=' answer'><span>One hot encoding<\/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-408141'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance. <br \/>\r<br>Which approach should the ML specialist use to determine the ideal data transformations for the model?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='408141' \/><input type='hidden' id='answerType408141' 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-408141[]' id='answer-id-1582004' class='answer   answerof-408141 ' value='1582004'   \/><label for='answer-id-1582004' id='answer-label-1582004' class=' answer'><span>Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408141[]' id='answer-id-1582005' class='answer   answerof-408141 ' value='1582005'   \/><label for='answer-id-1582005' id='answer-label-1582005' class=' answer'><span>Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408141[]' id='answer-id-1582006' class='answer   answerof-408141 ' value='1582006'   \/><label for='answer-id-1582006' id='answer-label-1582006' class=' answer'><span>Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408141[]' id='answer-id-1582007' class='answer   answerof-408141 ' value='1582007'   \/><label for='answer-id-1582007' id='answer-label-1582007' class=' answer'><span>Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.<\/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-408142'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings. <br \/>\r<br>To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities. <br \/>\r<br>Which deployment architecture for the model will address these business requirements?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='408142' \/><input type='hidden' id='answerType408142' 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-408142[]' id='answer-id-1582008' class='answer   answerof-408142 ' value='1582008'   \/><label for='answer-id-1582008' id='answer-label-1582008' class=' answer'><span>Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408142[]' id='answer-id-1582009' class='answer   answerof-408142 ' value='1582009'   \/><label for='answer-id-1582009' id='answer-label-1582009' class=' answer'><span>Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408142[]' id='answer-id-1582010' class='answer   answerof-408142 ' value='1582010'   \/><label for='answer-id-1582010' id='answer-label-1582010' class=' answer'><span>Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408142[]' id='answer-id-1582011' class='answer   answerof-408142 ' value='1582011'   \/><label for='answer-id-1582011' id='answer-label-1582011' class=' answer'><span>Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.<\/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-408143'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter. <br \/>\r<br>Which machine learning approach should be used to solve this problem?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='408143' \/><input type='hidden' id='answerType408143' 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-408143[]' id='answer-id-1582012' class='answer   answerof-408143 ' value='1582012'   \/><label for='answer-id-1582012' id='answer-label-1582012' class=' answer'><span>Logistic regression<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408143[]' id='answer-id-1582013' class='answer   answerof-408143 ' value='1582013'   \/><label for='answer-id-1582013' id='answer-label-1582013' class=' answer'><span>Random Cut Forest (RCF)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408143[]' id='answer-id-1582014' class='answer   answerof-408143 ' value='1582014'   \/><label for='answer-id-1582014' id='answer-label-1582014' class=' answer'><span>Principal component analysis (PCA)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408143[]' id='answer-id-1582015' class='answer   answerof-408143 ' value='1582015'   \/><label for='answer-id-1582015' id='answer-label-1582015' class=' answer'><span>Linear regression<\/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-408144'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A data scientist is building a linear regression model. The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean. <br \/>\r<br>Which data transformation will give the data scientist the ability to apply a linear regression model?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='408144' \/><input type='hidden' id='answerType408144' 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-408144[]' id='answer-id-1582016' class='answer   answerof-408144 ' value='1582016'   \/><label for='answer-id-1582016' id='answer-label-1582016' class=' answer'><span>Exponential transformation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408144[]' id='answer-id-1582017' class='answer   answerof-408144 ' value='1582017'   \/><label for='answer-id-1582017' id='answer-label-1582017' class=' answer'><span>Logarithmic transformation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408144[]' id='answer-id-1582018' class='answer   answerof-408144 ' value='1582018'   \/><label for='answer-id-1582018' id='answer-label-1582018' class=' answer'><span>Polynomial transformation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408144[]' id='answer-id-1582019' class='answer   answerof-408144 ' value='1582019'   \/><label for='answer-id-1582019' id='answer-label-1582019' class=' answer'><span>Sinusoidal transformation<\/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-408145'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data. <br \/>\r<br>Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_26' value='408145' \/><input type='hidden' id='answerType408145' 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-408145[]' id='answer-id-1582020' class='answer   answerof-408145 ' value='1582020'   \/><label for='answer-id-1582020' id='answer-label-1582020' class=' answer'><span>Use SageMaker Clarify to automatically detect data bias<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408145[]' id='answer-id-1582021' class='answer   answerof-408145 ' value='1582021'   \/><label for='answer-id-1582021' id='answer-label-1582021' class=' answer'><span>Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408145[]' id='answer-id-1582022' class='answer   answerof-408145 ' value='1582022'   \/><label for='answer-id-1582022' id='answer-label-1582022' class=' answer'><span>Use SageMaker Model Monitor to generate a bias drift report.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408145[]' id='answer-id-1582023' class='answer   answerof-408145 ' value='1582023'   \/><label for='answer-id-1582023' id='answer-label-1582023' class=' answer'><span>Configure SageMaker Data Wrangler to generate a bias report.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408145[]' id='answer-id-1582024' class='answer   answerof-408145 ' value='1582024'   \/><label for='answer-id-1582024' id='answer-label-1582024' class=' answer'><span>Use SageMaker Experiments to perform a data check<\/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-408146'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/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. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives. <br \/>\r<br><br><img decoding=\"async\" width=158 height=46 id=\"\u56fe\u7247 10\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/06\/image002-32.jpg\"><br><br \/>\r<br>Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_27' value='408146' \/><input type='hidden' id='answerType408146' 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-408146[]' id='answer-id-1582025' class='answer   answerof-408146 ' value='1582025'   \/><label for='answer-id-1582025' id='answer-label-1582025' class=' answer'><span>Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408146[]' id='answer-id-1582026' class='answer   answerof-408146 ' value='1582026'   \/><label for='answer-id-1582026' id='answer-label-1582026' 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-408146[]' id='answer-id-1582027' class='answer   answerof-408146 ' value='1582027'   \/><label for='answer-id-1582027' id='answer-label-1582027' 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-408146[]' id='answer-id-1582028' class='answer   answerof-408146 ' value='1582028'   \/><label for='answer-id-1582028' id='answer-label-1582028' class=' answer'><span>Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408146[]' id='answer-id-1582029' class='answer   answerof-408146 ' value='1582029'   \/><label for='answer-id-1582029' id='answer-label-1582029' 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-28' style=';'><div id='questionWrap-28'  class='   watupro-question-id-408147'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible. <br \/>\r<br>Which steps would improve the accuracy of the solution? (Choose three.)<\/div><input type='hidden' name='question_id[]' id='qID_28' value='408147' \/><input type='hidden' id='answerType408147' 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-408147[]' id='answer-id-1582030' class='answer   answerof-408147 ' value='1582030'   \/><label for='answer-id-1582030' id='answer-label-1582030' class=' answer'><span>Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408147[]' id='answer-id-1582031' class='answer   answerof-408147 ' value='1582031'   \/><label for='answer-id-1582031' id='answer-label-1582031' class=' answer'><span>Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408147[]' id='answer-id-1582032' class='answer   answerof-408147 ' value='1582032'   \/><label for='answer-id-1582032' id='answer-label-1582032' class=' answer'><span>Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408147[]' id='answer-id-1582033' class='answer   answerof-408147 ' value='1582033'   \/><label for='answer-id-1582033' id='answer-label-1582033' class=' answer'><span>Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. \r\nStore the new dataset in Amazon S3.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408147[]' id='answer-id-1582034' class='answer   answerof-408147 ' value='1582034'   \/><label for='answer-id-1582034' id='answer-label-1582034' class=' answer'><span>Use Amazon Rekognition Custom Labels to train a new model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-408147[]' id='answer-id-1582035' class='answer   answerof-408147 ' value='1582035'   \/><label for='answer-id-1582035' id='answer-label-1582035' class=' answer'><span>Check whether there are class imbalances in the product categories, and apply oversampling or \r\nundersampling as required. Store the new dataset in Amazon S3.<\/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-408148'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC. <br \/>\r<br>Why is the ML Specialist not seeing the instance visible in the VPC?<\/div><input type='hidden' name='question_id[]' id='qID_29' value='408148' \/><input type='hidden' id='answerType408148' 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-408148[]' id='answer-id-1582036' class='answer   answerof-408148 ' value='1582036'   \/><label for='answer-id-1582036' id='answer-label-1582036' class=' answer'><span>Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, butthey run outside of VPCs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408148[]' id='answer-id-1582037' class='answer   answerof-408148 ' value='1582037'   \/><label for='answer-id-1582037' id='answer-label-1582037' class=' answer'><span>Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408148[]' id='answer-id-1582038' class='answer   answerof-408148 ' value='1582038'   \/><label for='answer-id-1582038' id='answer-label-1582038' class=' answer'><span>Amazon SageMaker notebook instances are based on EC2 instances running within AWS serviceaccounts.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408148[]' id='answer-id-1582039' class='answer   answerof-408148 ' value='1582039'   \/><label for='answer-id-1582039' id='answer-label-1582039' class=' answer'><span>Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS serviceaccounts.<\/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-408149'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>A data scientist for a medical diagnostic testing company has developed a machine learning (ML) model to identify patients who have a specific disease. The dataset that the scientist used to train the model is imbalanced. The dataset contains a large number of healthy patients and only a small number of patients who have the disease. The model should consider that patients who are incorrectly identified as positive for the disease will increase costs for the company. <br \/>\r<br>Which metric will MOST accurately evaluate the performance of this model?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='408149' \/><input type='hidden' id='answerType408149' 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-408149[]' id='answer-id-1582040' class='answer   answerof-408149 ' value='1582040'   \/><label for='answer-id-1582040' id='answer-label-1582040' class=' answer'><span>Recall<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408149[]' id='answer-id-1582041' class='answer   answerof-408149 ' value='1582041'   \/><label for='answer-id-1582041' id='answer-label-1582041' class=' answer'><span>F1 score<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408149[]' id='answer-id-1582042' class='answer   answerof-408149 ' value='1582042'   \/><label for='answer-id-1582042' id='answer-label-1582042' class=' answer'><span>Accuracy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408149[]' id='answer-id-1582043' class='answer   answerof-408149 ' value='1582043'   \/><label for='answer-id-1582043' id='answer-label-1582043' class=' answer'><span>Precision<\/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-408150'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. <br \/>\r<br>What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?<\/div><input type='hidden' name='question_id[]' id='qID_31' value='408150' \/><input type='hidden' id='answerType408150' 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-408150[]' id='answer-id-1582044' class='answer   answerof-408150 ' value='1582044'   \/><label for='answer-id-1582044' id='answer-label-1582044' class=' answer'><span>Receiver operating characteristic (ROC) curve<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408150[]' id='answer-id-1582045' class='answer   answerof-408150 ' value='1582045'   \/><label for='answer-id-1582045' id='answer-label-1582045' class=' answer'><span>Misclassification rate<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408150[]' id='answer-id-1582046' class='answer   answerof-408150 ' value='1582046'   \/><label for='answer-id-1582046' id='answer-label-1582046' class=' answer'><span>Root Mean Square Error (RM&amp;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408150[]' id='answer-id-1582047' class='answer   answerof-408150 ' value='1582047'   \/><label for='answer-id-1582047' id='answer-label-1582047' class=' answer'><span>L1 norm<\/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-408151'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>A financial services company is building a robust serverless data lake on Amazon S3. <br \/>\r<br>The data lake should be flexible and meet the following requirements: <br \/>\r<br>* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum. <br \/>\r<br>* Support event-driven ETL pipelines. <br \/>\r<br>* Provide a quick and easy way to understand metadata. <br \/>\r<br>Which approach meets trfese requirements?<\/div><input type='hidden' name='question_id[]' id='qID_32' value='408151' \/><input type='hidden' id='answerType408151' 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-408151[]' id='answer-id-1582048' class='answer   answerof-408151 ' value='1582048'   \/><label for='answer-id-1582048' id='answer-label-1582048' class=' answer'><span>Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408151[]' id='answer-id-1582049' class='answer   answerof-408151 ' value='1582049'   \/><label for='answer-id-1582049' id='answer-label-1582049' class=' answer'><span>Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408151[]' id='answer-id-1582050' class='answer   answerof-408151 ' value='1582050'   \/><label for='answer-id-1582050' id='answer-label-1582050' class=' answer'><span>Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408151[]' id='answer-id-1582051' class='answer   answerof-408151 ' value='1582051'   \/><label for='answer-id-1582051' id='answer-label-1582051' class=' answer'><span>Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.<\/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-408152'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues. <br \/>\r<br>The Data Scientist on this project would like to speed up the model training time without losing a lot of <br \/>\r<br>information from the original dataset. <br \/>\r<br>Which feature engineering technique should the Data Scientist use to meet the objectives?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='408152' \/><input type='hidden' id='answerType408152' 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-408152[]' id='answer-id-1582052' class='answer   answerof-408152 ' value='1582052'   \/><label for='answer-id-1582052' id='answer-label-1582052' class=' answer'><span>Run self-correlation on all features and remove highly correlated features<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408152[]' id='answer-id-1582053' class='answer   answerof-408152 ' value='1582053'   \/><label for='answer-id-1582053' id='answer-label-1582053' class=' answer'><span>Normalize all numerical values to be between 0 and 1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408152[]' id='answer-id-1582054' class='answer   answerof-408152 ' value='1582054'   \/><label for='answer-id-1582054' id='answer-label-1582054' class=' answer'><span>Use an autoencoder or principal component analysis (PCA) to replace original features with new features<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408152[]' id='answer-id-1582055' class='answer   answerof-408152 ' value='1582055'   \/><label for='answer-id-1582055' id='answer-label-1582055' class=' answer'><span>Cluster raw data using k-means and use sample data from each cluster to build a new dataset<\/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-408153'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet. <br \/>\r<br>Which set of actions should the data science team take to fix the issue?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='408153' \/><input type='hidden' id='answerType408153' 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-408153[]' id='answer-id-1582056' class='answer   answerof-408153 ' value='1582056'   \/><label for='answer-id-1582056' id='answer-label-1582056' class=' answer'><span>Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408153[]' id='answer-id-1582057' class='answer   answerof-408153 ' value='1582057'   \/><label for='answer-id-1582057' id='answer-label-1582057' class=' answer'><span>Apply this security group to all of the notebook instances' VPC interfaces.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408153[]' id='answer-id-1582058' class='answer   answerof-408153 ' value='1582058'   \/><label for='answer-id-1582058' id='answer-label-1582058' class=' answer'><span>Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408153[]' id='answer-id-1582059' class='answer   answerof-408153 ' value='1582059'   \/><label for='answer-id-1582059' id='answer-label-1582059' class=' answer'><span>Add a NAT gateway to the VP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408153[]' id='answer-id-1582060' class='answer   answerof-408153 ' value='1582060'   \/><label for='answer-id-1582060' id='answer-label-1582060' class=' answer'><span>Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408153[]' id='answer-id-1582061' class='answer   answerof-408153 ' value='1582061'   \/><label for='answer-id-1582061' id='answer-label-1582061' class=' answer'><span>Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VP<\/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-408154'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed. <br \/>\r<br>The solution needs to do the following: <br \/>\r<br>Calculate an anomaly score for each web traffic entry. <br \/>\r<br>Adapt unusual event identification to changing web patterns over time. <br \/>\r<br>Which approach should the data scientist implement to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_35' value='408154' \/><input type='hidden' id='answerType408154' 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-408154[]' id='answer-id-1582062' class='answer   answerof-408154 ' value='1582062'   \/><label for='answer-id-1582062' id='answer-label-1582062' class=' answer'><span>Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408154[]' id='answer-id-1582063' class='answer   answerof-408154 ' value='1582063'   \/><label for='answer-id-1582063' id='answer-label-1582063' class=' answer'><span>Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408154[]' id='answer-id-1582064' class='answer   answerof-408154 ' value='1582064'   \/><label for='answer-id-1582064' id='answer-label-1582064' class=' answer'><span>Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408154[]' id='answer-id-1582065' class='answer   answerof-408154 ' value='1582065'   \/><label for='answer-id-1582065' id='answer-label-1582065' class=' answer'><span>Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.<\/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-408155'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model's hyperparameters. The ML engineer notices that the tuning jobs take a long time to run. The tuning jobs continue even when the jobs are not significantly improving against the objective metric. <br \/>\r<br>The ML engineer needs the training jobs to optimize the hyperparameters more quickly. <br \/>\r<br>How should the ML engineer configure the SageMaker AMT data types to meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_36' value='408155' \/><input type='hidden' id='answerType408155' 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-408155[]' id='answer-id-1582066' class='answer   answerof-408155 ' value='1582066'   \/><label for='answer-id-1582066' id='answer-label-1582066' class=' answer'><span>Set Strategy to the Bayesian value.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408155[]' id='answer-id-1582067' class='answer   answerof-408155 ' value='1582067'   \/><label for='answer-id-1582067' id='answer-label-1582067' class=' answer'><span>Set RetryStrategy to a value of 1.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408155[]' id='answer-id-1582068' class='answer   answerof-408155 ' value='1582068'   \/><label for='answer-id-1582068' id='answer-label-1582068' class=' answer'><span>Set ParameterRanges to the narrow range inferred from previous hyperparameter jobs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408155[]' id='answer-id-1582069' class='answer   answerof-408155 ' value='1582069'   \/><label for='answer-id-1582069' id='answer-label-1582069' class=' answer'><span>Set TrainingJobEarlyStoppingType to the AUTO value.<\/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-408156'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields. <br \/>\r<br>Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?<\/div><input type='hidden' name='question_id[]' id='qID_37' value='408156' \/><input type='hidden' id='answerType408156' 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-408156[]' id='answer-id-1582070' class='answer   answerof-408156 ' value='1582070'   \/><label for='answer-id-1582070' id='answer-label-1582070' class=' answer'><span>Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408156[]' id='answer-id-1582071' class='answer   answerof-408156 ' value='1582071'   \/><label for='answer-id-1582071' id='answer-label-1582071' class=' answer'><span>Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408156[]' id='answer-id-1582072' class='answer   answerof-408156 ' value='1582072'   \/><label for='answer-id-1582072' id='answer-label-1582072' class=' answer'><span>Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408156[]' id='answer-id-1582073' class='answer   answerof-408156 ' value='1582073'   \/><label for='answer-id-1582073' id='answer-label-1582073' class=' answer'><span>Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.<\/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-408157'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B, 240 samples for category C, 258 samples for category D, and 310 samples for category E. <br \/>\r<br>The data scientist shuffles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets. <br \/>\r<br>What could the data scientist conclude form these results?<\/div><input type='hidden' name='question_id[]' id='qID_38' value='408157' \/><input type='hidden' id='answerType408157' 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-408157[]' id='answer-id-1582074' class='answer   answerof-408157 ' value='1582074'   \/><label for='answer-id-1582074' id='answer-label-1582074' class=' answer'><span>Classes C and D are too similar.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408157[]' id='answer-id-1582075' class='answer   answerof-408157 ' value='1582075'   \/><label for='answer-id-1582075' id='answer-label-1582075' class=' answer'><span>The dataset is too small for holdout cross-validation.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408157[]' id='answer-id-1582076' class='answer   answerof-408157 ' value='1582076'   \/><label for='answer-id-1582076' id='answer-label-1582076' class=' answer'><span>The data distribution is skewed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408157[]' id='answer-id-1582077' class='answer   answerof-408157 ' value='1582077'   \/><label for='answer-id-1582077' id='answer-label-1582077' class=' answer'><span>The model is overfitting for classes B and<\/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-408158'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>A machine learning (ML) engineer is creating a binary classification model. The ML engineer will use the model in a highly sensitive environment. <br \/>\r<br>There is no cost associated with missing a positive label. However, the cost of making a false positive inference is extremely high. <br \/>\r<br>What is the most important metric to optimize the model for in this scenario?<\/div><input type='hidden' name='question_id[]' id='qID_39' value='408158' \/><input type='hidden' id='answerType408158' 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-408158[]' id='answer-id-1582078' class='answer   answerof-408158 ' value='1582078'   \/><label for='answer-id-1582078' id='answer-label-1582078' class=' answer'><span>Accuracy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408158[]' id='answer-id-1582079' class='answer   answerof-408158 ' value='1582079'   \/><label for='answer-id-1582079' id='answer-label-1582079' class=' answer'><span>Precision<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408158[]' id='answer-id-1582080' class='answer   answerof-408158 ' value='1582080'   \/><label for='answer-id-1582080' id='answer-label-1582080' class=' answer'><span>Recall<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408158[]' id='answer-id-1582081' class='answer   answerof-408158 ' value='1582081'   \/><label for='answer-id-1582081' id='answer-label-1582081' class=' answer'><span>F1<\/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-408159'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker. <br \/>\r<br>How can the data scientist meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='408159' \/><input type='hidden' id='answerType408159' 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-408159[]' id='answer-id-1582082' class='answer   answerof-408159 ' value='1582082'   \/><label for='answer-id-1582082' id='answer-label-1582082' class=' answer'><span>Call the CreateNotebookInstanceLifecycleConfig API operation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408159[]' id='answer-id-1582083' class='answer   answerof-408159 ' value='1582083'   \/><label for='answer-id-1582083' id='answer-label-1582083' class=' answer'><span>Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408159[]' id='answer-id-1582084' class='answer   answerof-408159 ' value='1582084'   \/><label for='answer-id-1582084' id='answer-label-1582084' class=' answer'><span>Stop and then restart the SageMaker notebook instance<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-408159[]' id='answer-id-1582085' class='answer   answerof-408159 ' value='1582085'   \/><label for='answer-id-1582085' id='answer-label-1582085' class=' answer'><span>Call the UpdateNotebookInstanceLifecycleConfig API operation<\/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=\"watuPROButtons10289\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; 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   \t \n<\/script>\n<p>&nbsp;<\/p>\n<h3>Amazon <a href=\"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\"><span style=\"background-color: #ffff99;\"><em>MLS-C01 free dumps (Part 3, Q81-Q120) of V13.02<\/em><\/span><\/a> are also available.<\/h3>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>You can trust that the MLS-C01 dumps (V13.02) are reliable for your AWS Certified Machine Learning &#8211; Specialty exam preparation, enabling you to develop effective strategies for answering questions accurately and efficiently. 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By [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[175,15637],"tags":[17460,17459],"class_list":["post-113345","post","type-post","status-publish","format-standard","hentry","category-amazon","category-aws-certification","tag-aws-certified-machine-learning-specialty","tag-mls-c01-dumps"],"_links":{"self":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/113345","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/comments?post=113345"}],"version-history":[{"count":3,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/113345\/revisions"}],"predecessor-version":[{"id":113879,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/113345\/revisions\/113879"}],"wp:attachment":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/media?parent=113345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/categories?post=113345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/tags?post=113345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}