{"id":82749,"date":"2024-06-25T08:20:45","date_gmt":"2024-06-25T08:20:45","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=82749"},"modified":"2024-06-25T08:20:50","modified_gmt":"2024-06-25T08:20:50","slug":"best-databricks-machine-learning-associate-exam-dumps-v8-02-tackle-your-databricks-certified-machine-learning-associate-exam-with-confidence","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/best-databricks-machine-learning-associate-exam-dumps-v8-02-tackle-your-databricks-certified-machine-learning-associate-exam-with-confidence.html","title":{"rendered":"Best Databricks Machine Learning Associate Exam Dumps (V8.02) &#8211; Tackle Your Databricks Certified Machine Learning Associate Exam with Confidence"},"content":{"rendered":"\n<p>Getting the Databricks Certified Machine Learning Associate certification shows that you are good at using Databricks to perform basic machine learning tasks. Also, it will prove that you are the machine learning associate keeping ahead in today&#8217;s rapidly changing environment. When preparing for the Databricks Certified Machine Learning Associate certification exam, DumpsBase is here to provide you with the best Databricks Machine Learning Associate exam dumps for learning. The current version of the Databricks Machine Learning Associate dumps is V8.02, which is the latest guide to help you confidently prepare for the exam and achieve your certification. Furthermore, these dumps are continuously updated, ensuring that you have the most up-to-date information in your hands. With the best Databricks Machine Learning Associate exam dumps (V8.02), you can carry your career to the next level with the Databricks Certified Machine Learning Associate certification.<\/p>\n<h2>Read <em><span style=\"background-color: #ffff00;\">Databricks Machine Learning Associate Free Dumps Below<\/span><\/em><\/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=\"submittingExam8733\" 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-8733\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-8733\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-339642'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically. <br \/>\r<br>Which of the following lines of code will return the metadata description?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='339642' \/><input type='hidden' id='answerType339642' 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-339642[]' id='answer-id-1328772' class='answer   answerof-339642 ' value='1328772'   \/><label for='answer-id-1328772' id='answer-label-1328772' class=' answer'><span>There is no way to return the metadata description programmatically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339642[]' id='answer-id-1328773' class='answer   answerof-339642 ' value='1328773'   \/><label for='answer-id-1328773' id='answer-label-1328773' class=' answer'><span>fs.create_training_set(&quot;new_table&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339642[]' id='answer-id-1328774' class='answer   answerof-339642 ' value='1328774'   \/><label for='answer-id-1328774' id='answer-label-1328774' class=' answer'><span>fs.get_table(&quot;new_table&quot;).description<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339642[]' id='answer-id-1328775' class='answer   answerof-339642 ' value='1328775'   \/><label for='answer-id-1328775' id='answer-label-1328775' class=' answer'><span>fs.get_table(&quot;new_table&quot;).load_df()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339642[]' id='answer-id-1328776' class='answer   answerof-339642 ' value='1328776'   \/><label for='answer-id-1328776' id='answer-label-1328776' class=' answer'><span>fs.get_table(&quot;new_table&quot;)<\/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-339643'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column price is greater than 0. <br \/>\r<br>Which of the following code blocks will accomplish this task?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='339643' \/><input type='hidden' id='answerType339643' 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-339643[]' id='answer-id-1328777' class='answer   answerof-339643 ' value='1328777'   \/><label for='answer-id-1328777' id='answer-label-1328777' class=' answer'><span>spark_df[spark_df[&quot;price&quot;] &gt; 0]<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339643[]' id='answer-id-1328778' class='answer   answerof-339643 ' value='1328778'   \/><label for='answer-id-1328778' id='answer-label-1328778' class=' answer'><span>spark_df.filter(col(&quot;price&quot;) &gt; 0)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339643[]' id='answer-id-1328779' class='answer   answerof-339643 ' value='1328779'   \/><label for='answer-id-1328779' id='answer-label-1328779' class=' answer'><span>SELECT * FROM spark_df WHERE price &gt; 0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339643[]' id='answer-id-1328780' class='answer   answerof-339643 ' value='1328780'   \/><label for='answer-id-1328780' id='answer-label-1328780' class=' answer'><span>spark_df.loc[spark_df[&quot;price&quot;] &gt; 0,:]<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339643[]' id='answer-id-1328781' class='answer   answerof-339643 ' value='1328781'   \/><label for='answer-id-1328781' id='answer-label-1328781' class=' answer'><span>spark_df.loc[:,spark_df[&quot;price&quot;] &gt; 0]<\/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-339644'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model. <br \/>\r<br>Which of the following classification metrics should be used to evaluate the model?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='339644' \/><input type='hidden' id='answerType339644' 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-339644[]' id='answer-id-1328782' class='answer   answerof-339644 ' value='1328782'   \/><label for='answer-id-1328782' id='answer-label-1328782' class=' answer'><span>RMSE<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339644[]' id='answer-id-1328783' class='answer   answerof-339644 ' value='1328783'   \/><label for='answer-id-1328783' id='answer-label-1328783' class=' answer'><span>Precision<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339644[]' id='answer-id-1328784' class='answer   answerof-339644 ' value='1328784'   \/><label for='answer-id-1328784' id='answer-label-1328784' class=' answer'><span>Area under the residual operating curve<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339644[]' id='answer-id-1328785' class='answer   answerof-339644 ' value='1328785'   \/><label for='answer-id-1328785' id='answer-label-1328785' class=' answer'><span>Accuracy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339644[]' id='answer-id-1328786' class='answer   answerof-339644 ' value='1328786'   \/><label for='answer-id-1328786' id='answer-label-1328786' class=' answer'><span>Recall<\/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-339645'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='339645' \/><input type='hidden' id='answerType339645' 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-339645[]' id='answer-id-1328787' class='answer   answerof-339645 ' value='1328787'   \/><label for='answer-id-1328787' id='answer-label-1328787' class=' answer'><span>When the features are of the categorical type<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339645[]' id='answer-id-1328788' class='answer   answerof-339645 ' value='1328788'   \/><label for='answer-id-1328788' id='answer-label-1328788' class=' answer'><span>When the features are of the boolean type<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339645[]' id='answer-id-1328789' class='answer   answerof-339645 ' value='1328789'   \/><label for='answer-id-1328789' id='answer-label-1328789' class=' answer'><span>When the features contain a lot of extreme outliers<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339645[]' id='answer-id-1328790' class='answer   answerof-339645 ' value='1328790'   \/><label for='answer-id-1328790' id='answer-label-1328790' class=' answer'><span>When the features contain no outliers<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339645[]' id='answer-id-1328791' class='answer   answerof-339645 ' value='1328791'   \/><label for='answer-id-1328791' id='answer-label-1328791' class=' answer'><span>When the features contain no missing no values<\/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-339646'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>A data scientist has replaced missing values in their feature set with each respective feature variable\u2019s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this. <br \/>\r<br>Which of the following approaches can they take to include as much information as possible in the feature set?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='339646' \/><input type='hidden' id='answerType339646' 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-339646[]' id='answer-id-1328792' class='answer   answerof-339646 ' value='1328792'   \/><label for='answer-id-1328792' id='answer-label-1328792' class=' answer'><span>Impute the missing values using each respective feature variable's mean value instead of the median value<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339646[]' id='answer-id-1328793' class='answer   answerof-339646 ' value='1328793'   \/><label for='answer-id-1328793' id='answer-label-1328793' class=' answer'><span>Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339646[]' id='answer-id-1328794' class='answer   answerof-339646 ' value='1328794'   \/><label for='answer-id-1328794' id='answer-label-1328794' class=' answer'><span>Remove all feature variables that originally contained missing values from the feature set<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339646[]' id='answer-id-1328795' class='answer   answerof-339646 ' value='1328795'   \/><label for='answer-id-1328795' id='answer-label-1328795' class=' answer'><span>Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339646[]' id='answer-id-1328796' class='answer   answerof-339646 ' value='1328796'   \/><label for='answer-id-1328796' id='answer-label-1328796' class=' answer'><span>Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing<\/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-339647'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature. <br \/>\r<br>Which of the following lines of code can the data scientist run to accomplish the task?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='339647' \/><input type='hidden' id='answerType339647' 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-339647[]' id='answer-id-1328797' class='answer   answerof-339647 ' value='1328797'   \/><label for='answer-id-1328797' id='answer-label-1328797' class=' answer'><span>spark_df.summary ()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339647[]' id='answer-id-1328798' class='answer   answerof-339647 ' value='1328798'   \/><label for='answer-id-1328798' id='answer-label-1328798' class=' answer'><span>spark_df.stats()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339647[]' id='answer-id-1328799' class='answer   answerof-339647 ' value='1328799'   \/><label for='answer-id-1328799' id='answer-label-1328799' class=' answer'><span>spark_df.describe().head()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339647[]' id='answer-id-1328800' class='answer   answerof-339647 ' value='1328800'   \/><label for='answer-id-1328800' id='answer-label-1328800' class=' answer'><span>spark_df.printSchema()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339647[]' id='answer-id-1328801' class='answer   answerof-339647 ' value='1328801'   \/><label for='answer-id-1328801' id='answer-label-1328801' class=' answer'><span>spark_df.toPandas()<\/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-339648'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository. <br \/>\r<br>Which of the following explanations justifies this suggestion?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='339648' \/><input type='hidden' id='answerType339648' 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-339648[]' id='answer-id-1328802' class='answer   answerof-339648 ' value='1328802'   \/><label for='answer-id-1328802' id='answer-label-1328802' class=' answer'><span>One-hot encoding is not supported by most machine learning libraries.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339648[]' id='answer-id-1328803' class='answer   answerof-339648 ' value='1328803'   \/><label for='answer-id-1328803' id='answer-label-1328803' class=' answer'><span>One-hot encoding is dependent on the target variable's values which differ for each application.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339648[]' id='answer-id-1328804' class='answer   answerof-339648 ' value='1328804'   \/><label for='answer-id-1328804' id='answer-label-1328804' class=' answer'><span>One-hot encoding is computationally intensive and should only be performed on small samples of \r\ntraining sets for individual machine learning problems.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339648[]' id='answer-id-1328805' class='answer   answerof-339648 ' value='1328805'   \/><label for='answer-id-1328805' id='answer-label-1328805' class=' answer'><span>One-hot encoding is not a common strategy for representing categorical feature variables numerically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339648[]' id='answer-id-1328806' class='answer   answerof-339648 ' value='1328806'   \/><label for='answer-id-1328806' id='answer-label-1328806' class=' answer'><span>One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.<\/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-339649'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model by comparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model. <br \/>\r<br>Which of the following possible explanations for this difference is invalid?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='339649' \/><input type='hidden' id='answerType339649' 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-339649[]' id='answer-id-1328807' class='answer   answerof-339649 ' value='1328807'   \/><label for='answer-id-1328807' id='answer-label-1328807' class=' answer'><span>The second model is much more accurate than the first model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339649[]' id='answer-id-1328808' class='answer   answerof-339649 ' value='1328808'   \/><label for='answer-id-1328808' id='answer-label-1328808' class=' answer'><span>The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339649[]' id='answer-id-1328809' class='answer   answerof-339649 ' value='1328809'   \/><label for='answer-id-1328809' id='answer-label-1328809' class=' answer'><span>The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339649[]' id='answer-id-1328810' class='answer   answerof-339649 ' value='1328810'   \/><label for='answer-id-1328810' id='answer-label-1328810' class=' answer'><span>The first model is much more accurate than the second model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339649[]' id='answer-id-1328811' class='answer   answerof-339649 ' value='1328811'   \/><label for='answer-id-1328811' id='answer-label-1328811' class=' answer'><span>The RMSE is an invalid evaluation metric for regression problems<\/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-339650'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. <br \/>\r<br>The following root-mean-squared-error values are calculated on each of the validation folds: <br \/>\r<br>&#8226; 10.0 <br \/>\r<br>&#8226; 12.0 <br \/>\r<br>&#8226; 17.0 <br \/>\r<br>Which of the following values represents the overall cross-validation root-mean-squared error?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='339650' \/><input type='hidden' id='answerType339650' 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-339650[]' id='answer-id-1328812' class='answer   answerof-339650 ' value='1328812'   \/><label for='answer-id-1328812' id='answer-label-1328812' class=' answer'><span>13.0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339650[]' id='answer-id-1328813' class='answer   answerof-339650 ' value='1328813'   \/><label for='answer-id-1328813' id='answer-label-1328813' class=' answer'><span>17.0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339650[]' id='answer-id-1328814' class='answer   answerof-339650 ' value='1328814'   \/><label for='answer-id-1328814' id='answer-label-1328814' class=' answer'><span>12.0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339650[]' id='answer-id-1328815' class='answer   answerof-339650 ' value='1328815'   \/><label for='answer-id-1328815' id='answer-label-1328815' class=' answer'><span>39.0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339650[]' id='answer-id-1328816' class='answer   answerof-339650 ' value='1328816'   \/><label for='answer-id-1328816' id='answer-label-1328816' class=' answer'><span>10.0<\/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-339651'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. <br \/>\r<br>As part of the cross-validation process, they are using the following code block: <br \/>\r<br><br><img decoding=\"async\" width=391 height=213 id=\"\u56fe\u7247 32\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image002-27.jpg\"><br><br \/>\r<br>A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model. <br \/>\r<br>Which of the following is a negative consequence of the approach suggested by the colleague?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='339651' \/><input type='hidden' id='answerType339651' 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-339651[]' id='answer-id-1328817' class='answer   answerof-339651 ' value='1328817'   \/><label for='answer-id-1328817' id='answer-label-1328817' class=' answer'><span>The model will take longer to train for each unique combination of hvperparameter values<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339651[]' id='answer-id-1328818' class='answer   answerof-339651 ' value='1328818'   \/><label for='answer-id-1328818' id='answer-label-1328818' class=' answer'><span>The feature engineering stages will be computed using validation data<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339651[]' id='answer-id-1328819' class='answer   answerof-339651 ' value='1328819'   \/><label for='answer-id-1328819' id='answer-label-1328819' class=' answer'><span>The cross-validation process will no longer be<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339651[]' id='answer-id-1328820' class='answer   answerof-339651 ' value='1328820'   \/><label for='answer-id-1328820' id='answer-label-1328820' class=' answer'><span>The cross-validation process will no longer be reproducible<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339651[]' id='answer-id-1328821' class='answer   answerof-339651 ' value='1328821'   \/><label for='answer-id-1328821' id='answer-label-1328821' class=' answer'><span>The model will be refit one more per cross-validation fold<\/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-339652'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>What is the name of the method that transforms categorical features into a series of binary indicator feature variables?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='339652' \/><input type='hidden' id='answerType339652' 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-339652[]' id='answer-id-1328822' class='answer   answerof-339652 ' value='1328822'   \/><label for='answer-id-1328822' id='answer-label-1328822' class=' answer'><span>Leave-one-out encoding<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339652[]' id='answer-id-1328823' class='answer   answerof-339652 ' value='1328823'   \/><label for='answer-id-1328823' id='answer-label-1328823' class=' answer'><span>Target encoding<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339652[]' id='answer-id-1328824' class='answer   answerof-339652 ' value='1328824'   \/><label for='answer-id-1328824' id='answer-label-1328824' class=' answer'><span>One-hot encoding<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339652[]' id='answer-id-1328825' class='answer   answerof-339652 ' value='1328825'   \/><label for='answer-id-1328825' id='answer-label-1328825' class=' answer'><span>Categorical<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339652[]' id='answer-id-1328826' class='answer   answerof-339652 ' value='1328826'   \/><label for='answer-id-1328826' id='answer-label-1328826' class=' answer'><span>String indexing<\/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-339653'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult. <br \/>\r<br>Which of the following describes why?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='339653' \/><input type='hidden' id='answerType339653' 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-339653[]' id='answer-id-1328827' class='answer   answerof-339653 ' value='1328827'   \/><label for='answer-id-1328827' id='answer-label-1328827' class=' answer'><span>Gradient boosting is not a linear algebra-based algorithm which is required for parallelization<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339653[]' id='answer-id-1328828' class='answer   answerof-339653 ' value='1328828'   \/><label for='answer-id-1328828' id='answer-label-1328828' class=' answer'><span>Gradient boosting requires access to all data at once which cannot happen during parallelization.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339653[]' id='answer-id-1328829' class='answer   answerof-339653 ' value='1328829'   \/><label for='answer-id-1328829' id='answer-label-1328829' class=' answer'><span>Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339653[]' id='answer-id-1328830' class='answer   answerof-339653 ' value='1328830'   \/><label for='answer-id-1328830' id='answer-label-1328830' class=' answer'><span>Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.<\/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-339654'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin. <br \/>\r<br>They use the following code block to create the objective_function: <br \/>\r<br><br><img decoding=\"async\" width=649 height=205 id=\"\u56fe\u7247 31\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image003-26.jpg\"><br><br \/>\r<br>Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='339654' \/><input type='hidden' id='answerType339654' 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-339654[]' id='answer-id-1328831' class='answer   answerof-339654 ' value='1328831'   \/><label for='answer-id-1328831' id='answer-label-1328831' class=' answer'><span>Add test set validation process<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339654[]' id='answer-id-1328832' class='answer   answerof-339654 ' value='1328832'   \/><label for='answer-id-1328832' id='answer-label-1328832' class=' answer'><span>Add a random_state argument to the RandomForestRegressor operation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339654[]' id='answer-id-1328833' class='answer   answerof-339654 ' value='1328833'   \/><label for='answer-id-1328833' id='answer-label-1328833' class=' answer'><span>Remove the mean operation that is wrapping the cross_val_score operation<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339654[]' id='answer-id-1328834' class='answer   answerof-339654 ' value='1328834'   \/><label for='answer-id-1328834' id='answer-label-1328834' class=' answer'><span>Replace the r2 return value with -r2<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339654[]' id='answer-id-1328835' class='answer   answerof-339654 ' value='1328835'   \/><label for='answer-id-1328835' id='answer-label-1328835' class=' answer'><span>Replace the fmin operation with the fmax operation<\/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-339655'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation. <br \/>\r<br>They attempt to run the following code block, but it does not accomplish the desired task: <br \/>\r<br><br><img decoding=\"async\" width=651 height=63 id=\"\u56fe\u7247 30\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image004-23.jpg\"><br><br \/>\r<br>Which of the following changes can the data scientist make to accomplish the task?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='339655' \/><input type='hidden' id='answerType339655' 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-339655[]' id='answer-id-1328836' class='answer   answerof-339655 ' value='1328836'   \/><label for='answer-id-1328836' id='answer-label-1328836' class=' answer'><span>Replace the GridSearchCV operation with RandomizedSearchCV<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339655[]' id='answer-id-1328837' class='answer   answerof-339655 ' value='1328837'   \/><label for='answer-id-1328837' id='answer-label-1328837' class=' answer'><span>Replace the GridSearchCV operation with cross_validate<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339655[]' id='answer-id-1328838' class='answer   answerof-339655 ' value='1328838'   \/><label for='answer-id-1328838' id='answer-label-1328838' class=' answer'><span>Replace the GridSearchCV operation with ParameterGrid<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339655[]' id='answer-id-1328839' class='answer   answerof-339655 ' value='1328839'   \/><label for='answer-id-1328839' id='answer-label-1328839' class=' answer'><span>Replace the random_state=0 argument with random_state=1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339655[]' id='answer-id-1328840' class='answer   answerof-339655 ' value='1328840'   \/><label for='answer-id-1328840' id='answer-label-1328840' class=' answer'><span>Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')<\/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-339656'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='339656' \/><input type='hidden' id='answerType339656' 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-339656[]' id='answer-id-1328841' class='answer   answerof-339656 ' value='1328841'   \/><label for='answer-id-1328841' id='answer-label-1328841' class=' answer'><span>MLflow Experiment Tracking<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339656[]' id='answer-id-1328842' class='answer   answerof-339656 ' value='1328842'   \/><label for='answer-id-1328842' id='answer-label-1328842' class=' answer'><span>Spark ML<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339656[]' id='answer-id-1328843' class='answer   answerof-339656 ' value='1328843'   \/><label for='answer-id-1328843' id='answer-label-1328843' class=' answer'><span>Autoscaling clusters<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339656[]' id='answer-id-1328844' class='answer   answerof-339656 ' value='1328844'   \/><label for='answer-id-1328844' id='answer-label-1328844' class=' answer'><span>Autoscaling clusters<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339656[]' id='answer-id-1328845' class='answer   answerof-339656 ' value='1328845'   \/><label for='answer-id-1328845' id='answer-label-1328845' class=' answer'><span>Delta Lake<\/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-339657'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='339657' \/><input type='hidden' id='answerType339657' 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-339657[]' id='answer-id-1328846' class='answer   answerof-339657 ' value='1328846'   \/><label for='answer-id-1328846' id='answer-label-1328846' class=' answer'><span>pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339657[]' id='answer-id-1328847' class='answer   answerof-339657 ' value='1328847'   \/><label for='answer-id-1328847' id='answer-label-1328847' class=' answer'><span>pandas API on Spark DataFrames are more performant than Spark DataFrames<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339657[]' id='answer-id-1328848' class='answer   answerof-339657 ' value='1328848'   \/><label for='answer-id-1328848' id='answer-label-1328848' class=' answer'><span>pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339657[]' id='answer-id-1328849' class='answer   answerof-339657 ' value='1328849'   \/><label for='answer-id-1328849' id='answer-label-1328849' class=' answer'><span>pandas API on Spark DataFrames are less mutable versions of Spark DataFrames<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339657[]' id='answer-id-1328850' class='answer   answerof-339657 ' value='1328850'   \/><label for='answer-id-1328850' id='answer-label-1328850' class=' answer'><span>pandas API on Spark DataFrames are unrelated to Spark DataFrames<\/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-339658'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data. <br \/>\r<br>Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='339658' \/><input type='hidden' id='answerType339658' 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-339658[]' id='answer-id-1328851' class='answer   answerof-339658 ' value='1328851'   \/><label for='answer-id-1328851' id='answer-label-1328851' class=' answer'><span>They can refactor their notebook to process the data in parallel.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339658[]' id='answer-id-1328852' class='answer   answerof-339658 ' value='1328852'   \/><label for='answer-id-1328852' id='answer-label-1328852' class=' answer'><span>They can refactor their notebook to use the PySpark DataFrame AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339658[]' id='answer-id-1328853' class='answer   answerof-339658 ' value='1328853'   \/><label for='answer-id-1328853' id='answer-label-1328853' class=' answer'><span>They can refactor their notebook to use the Scala Dataset AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339658[]' id='answer-id-1328854' class='answer   answerof-339658 ' value='1328854'   \/><label for='answer-id-1328854' id='answer-label-1328854' class=' answer'><span>They can refactor their notebook to use Spark SQ<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339658[]' id='answer-id-1328855' class='answer   answerof-339658 ' value='1328855'   \/><label for='answer-id-1328855' id='answer-label-1328855' class=' answer'><span>They can refactor their notebook to utilize the pandas API on Spark.<\/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-339659'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model: <br \/>\r<br><br><img decoding=\"async\" width=651 height=152 id=\"\u56fe\u7247 29\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image005-19.jpg\"><br><br \/>\r<br>They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df: <br \/>\r<br><br><img decoding=\"async\" width=420 height=102 id=\"\u56fe\u7247 28\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image006-17.jpg\"><br><br \/>\r<br>Which of the following lines of code can be used to complete the code block to successfully complete the task?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='339659' \/><input type='hidden' id='answerType339659' 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-339659[]' id='answer-id-1328856' class='answer   answerof-339659 ' value='1328856'   \/><label for='answer-id-1328856' id='answer-label-1328856' class=' answer'><span>predict(*spark_df.columns)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339659[]' id='answer-id-1328857' class='answer   answerof-339659 ' value='1328857'   \/><label for='answer-id-1328857' id='answer-label-1328857' class=' answer'><span>mapInPandas(predict)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339659[]' id='answer-id-1328858' class='answer   answerof-339659 ' value='1328858'   \/><label for='answer-id-1328858' id='answer-label-1328858' class=' answer'><span>predict(Iterator(spark_df))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339659[]' id='answer-id-1328859' class='answer   answerof-339659 ' value='1328859'   \/><label for='answer-id-1328859' id='answer-label-1328859' class=' answer'><span>mapInPandas(predict(spark_df.columns))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339659[]' id='answer-id-1328860' class='answer   answerof-339659 ' value='1328860'   \/><label for='answer-id-1328860' id='answer-label-1328860' class=' answer'><span>predict(spark_df.columns)<\/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-339660'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='339660' \/><input type='hidden' id='answerType339660' 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-339660[]' id='answer-id-1328861' class='answer   answerof-339660 ' value='1328861'   \/><label for='answer-id-1328861' id='answer-label-1328861' class=' answer'><span>TrainValidationSplit<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339660[]' id='answer-id-1328862' class='answer   answerof-339660 ' value='1328862'   \/><label for='answer-id-1328862' id='answer-label-1328862' class=' answer'><span>DataFrame.where<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339660[]' id='answer-id-1328863' class='answer   answerof-339660 ' value='1328863'   \/><label for='answer-id-1328863' id='answer-label-1328863' class=' answer'><span>CrossValidator<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339660[]' id='answer-id-1328864' class='answer   answerof-339660 ' value='1328864'   \/><label for='answer-id-1328864' id='answer-label-1328864' class=' answer'><span>TrainValidationSplitModel<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339660[]' id='answer-id-1328865' class='answer   answerof-339660 ' value='1328865'   \/><label for='answer-id-1328865' id='answer-label-1328865' class=' answer'><span>DataFrame.randomSplit<\/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-339661'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A data scientist is using Spark ML to engineer features for an exploratory machine learning project. <br \/>\r<br>They decide they want to standardize their features using the following code block: <br \/>\r<br><br><img decoding=\"async\" width=650 height=201 id=\"\u56fe\u7247 27\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image007-17.jpg\"><br><br \/>\r<br>Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set. <br \/>\r<br>Which of the following changes can the data scientist make to address the concern?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='339661' \/><input type='hidden' id='answerType339661' 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-339661[]' id='answer-id-1328866' class='answer   answerof-339661 ' value='1328866'   \/><label for='answer-id-1328866' id='answer-label-1328866' class=' answer'><span>Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339661[]' id='answer-id-1328867' class='answer   answerof-339661 ' value='1328867'   \/><label for='answer-id-1328867' id='answer-label-1328867' class=' answer'><span>Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339661[]' id='answer-id-1328868' class='answer   answerof-339661 ' value='1328868'   \/><label for='answer-id-1328868' id='answer-label-1328868' class=' answer'><span>Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339661[]' id='answer-id-1328869' class='answer   answerof-339661 ' value='1328869'   \/><label for='answer-id-1328869' id='answer-label-1328869' class=' answer'><span>Utilize the Pipeline API to standardize the training data according to the test data's summary statistics<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339661[]' id='answer-id-1328870' class='answer   answerof-339661 ' value='1328870'   \/><label for='answer-id-1328870' id='answer-label-1328870' class=' answer'><span>Utilize the Pipeline API to standardize the test data according to the training data's summary statistics<\/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-339662'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process. <br \/>\r<br>Which of the following feature engineering tasks will be the least efficient to distribute?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='339662' \/><input type='hidden' id='answerType339662' 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-339662[]' id='answer-id-1328871' class='answer   answerof-339662 ' value='1328871'   \/><label for='answer-id-1328871' id='answer-label-1328871' class=' answer'><span>One-hot encoding categorical features<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339662[]' id='answer-id-1328872' class='answer   answerof-339662 ' value='1328872'   \/><label for='answer-id-1328872' id='answer-label-1328872' class=' answer'><span>Target encoding categorical features<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339662[]' id='answer-id-1328873' class='answer   answerof-339662 ' value='1328873'   \/><label for='answer-id-1328873' id='answer-label-1328873' class=' answer'><span>Imputing missing feature values with the mean<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339662[]' id='answer-id-1328874' class='answer   answerof-339662 ' value='1328874'   \/><label for='answer-id-1328874' id='answer-label-1328874' class=' answer'><span>Imputing missing feature values with the true median<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339662[]' id='answer-id-1328875' class='answer   answerof-339662 ' value='1328875'   \/><label for='answer-id-1328875' id='answer-label-1328875' class=' answer'><span>Creating binary indicator features for missing values<\/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-339663'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='339663' \/><input type='hidden' id='answerType339663' 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-339663[]' id='answer-id-1328876' class='answer   answerof-339663 ' value='1328876'   \/><label for='answer-id-1328876' id='answer-label-1328876' class=' answer'><span>The vectorized pandas UDFs allow for the use of type hints<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339663[]' id='answer-id-1328877' class='answer   answerof-339663 ' value='1328877'   \/><label for='answer-id-1328877' id='answer-label-1328877' class=' answer'><span>The vectorized pandas UDFs process data in batches rather than one row at a time<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339663[]' id='answer-id-1328878' class='answer   answerof-339663 ' value='1328878'   \/><label for='answer-id-1328878' id='answer-label-1328878' class=' answer'><span>The vectorized pandas UDFs allow for pandas API use inside of the function<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339663[]' id='answer-id-1328879' class='answer   answerof-339663 ' value='1328879'   \/><label for='answer-id-1328879' id='answer-label-1328879' class=' answer'><span>The vectorized pandas UDFs work on distributed DataFrames<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339663[]' id='answer-id-1328880' class='answer   answerof-339663 ' value='1328880'   \/><label for='answer-id-1328880' id='answer-label-1328880' class=' answer'><span>The vectorized pandas UDFs process data in memory rather than spilling to disk<\/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-339664'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space. <br \/>\r<br>As a result, they have the following code block: <br \/>\r<br><br><img decoding=\"async\" width=299 height=240 id=\"\u56fe\u7247 26\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image008-16.jpg\"><br><br \/>\r<br>Which of the following changes do they need to make to the above code block in order to accomplish the task?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='339664' \/><input type='hidden' id='answerType339664' 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-339664[]' id='answer-id-1328881' class='answer   answerof-339664 ' value='1328881'   \/><label for='answer-id-1328881' id='answer-label-1328881' class=' answer'><span>Change SparkTrials() to Trials()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339664[]' id='answer-id-1328882' class='answer   answerof-339664 ' value='1328882'   \/><label for='answer-id-1328882' id='answer-label-1328882' class=' answer'><span>Reduce num_evals to be less than 10<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339664[]' id='answer-id-1328883' class='answer   answerof-339664 ' value='1328883'   \/><label for='answer-id-1328883' id='answer-label-1328883' class=' answer'><span>Change fmin() to fmax()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339664[]' id='answer-id-1328884' class='answer   answerof-339664 ' value='1328884'   \/><label for='answer-id-1328884' id='answer-label-1328884' class=' answer'><span>Remove the trials=trials argument<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339664[]' id='answer-id-1328885' class='answer   answerof-339664 ' value='1328885'   \/><label for='answer-id-1328885' id='answer-label-1328885' class=' answer'><span>Remove the algo=tpe.suggest argument<\/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-339665'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model. <br \/>\r<br>The Spark DataFrame train_df has the following schema: <br \/>\r<br><br><img decoding=\"async\" width=240 height=71 id=\"\u56fe\u7247 25\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image009-15.jpg\"><br><br \/>\r<br>The machine learning engineer shares the following code block: <br \/>\r<br><br><img decoding=\"async\" width=650 height=43 id=\"\u56fe\u7247 24\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2024\/06\/image010-12.jpg\"><br><br \/>\r<br>Which of the following changes does the machine learning engineer need to make to complete the task?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='339665' \/><input type='hidden' id='answerType339665' 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-339665[]' id='answer-id-1328886' class='answer   answerof-339665 ' value='1328886'   \/><label for='answer-id-1328886' id='answer-label-1328886' class=' answer'><span>They need to call the transform method on train df<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339665[]' id='answer-id-1328887' class='answer   answerof-339665 ' value='1328887'   \/><label for='answer-id-1328887' id='answer-label-1328887' class=' answer'><span>They need to convert the features column to be a vector<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339665[]' id='answer-id-1328888' class='answer   answerof-339665 ' value='1328888'   \/><label for='answer-id-1328888' id='answer-label-1328888' class=' answer'><span>They do not need to make any changes<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339665[]' id='answer-id-1328889' class='answer   answerof-339665 ' value='1328889'   \/><label for='answer-id-1328889' id='answer-label-1328889' class=' answer'><span>They need to utilize a Pipeline to fit the model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339665[]' id='answer-id-1328890' class='answer   answerof-339665 ' value='1328890'   \/><label for='answer-id-1328890' id='answer-label-1328890' class=' answer'><span>They need to split the features column out into one column for each feature<\/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-339666'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='339666' \/><input type='hidden' id='answerType339666' 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-339666[]' id='answer-id-1328891' class='answer   answerof-339666 ' value='1328891'   \/><label for='answer-id-1328891' id='answer-label-1328891' class=' answer'><span>Keras<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339666[]' id='answer-id-1328892' class='answer   answerof-339666 ' value='1328892'   \/><label for='answer-id-1328892' id='answer-label-1328892' class=' answer'><span>pandas<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339666[]' id='answer-id-1328893' class='answer   answerof-339666 ' value='1328893'   \/><label for='answer-id-1328893' id='answer-label-1328893' class=' answer'><span>PvTorch<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339666[]' id='answer-id-1328894' class='answer   answerof-339666 ' value='1328894'   \/><label for='answer-id-1328894' id='answer-label-1328894' class=' answer'><span>Spark ML<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-339666[]' id='answer-id-1328895' class='answer   answerof-339666 ' value='1328895'   \/><label for='answer-id-1328895' id='answer-label-1328895' class=' answer'><span>Scikit-learn<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-26'>\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\" 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