{"id":112878,"date":"2025-10-27T08:10:09","date_gmt":"2025-10-27T08:10:09","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=112878"},"modified":"2025-10-27T08:10:38","modified_gmt":"2025-10-27T08:10:38","slug":"databricks-certified-associate-developer-for-apache-spark-3-5-dumps-v8-02-complete-your-exam-with-reliable-study-materials","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/databricks-certified-associate-developer-for-apache-spark-3-5-dumps-v8-02-complete-your-exam-with-reliable-study-materials.html","title":{"rendered":"Databricks Certified Associate Developer for Apache Spark 3.5 Dumps (V8.02) &#8211; Complete Your Exam with Reliable Study Materials"},"content":{"rendered":"<p>Focus on the latest information, the Databricks Certified Associate Developer for Apache Spark has been upgraded to version 3.5. Now, you must register for the Databricks Certified Associate Developer for Apache Spark 3.5 exam and achieve success smoothly. Preparing for the Databricks Certified Associate Developer for Apache Spark 3.5 exam has become essential for professionals aiming to achieve success on the first attempt. Our <a href=\"https:\/\/www.dumpsbase.com\/databricks.html\"><em><strong>Databricks<\/strong><\/em><\/a> Certified Associate Developer for Apache Spark 3.5 dumps (V8.02) ensure you stay ahead with the latest exam objectives, helping you pass the actual exam today. By relying on our verified Databricks Certified Associate Developer for Apache Spark 3.5 exam questions and answers, you will have a realistic experience, boosting confidence and improving performance before attempting the actual exam.<\/p>\n<h2>Read our <span style=\"background-color: #ffff00;\"><em>Databricks Certified Associate Developer for Apache Spark 3.5 free dumps below<\/em><\/span> first:<\/h2>\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam11032\" 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-11032\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11032\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-434420'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>You have: <br \/>\r<br>DataFrame A: 128 GB of transactions <br \/>\r<br>DataFrame B: 1 GB user lookup table <br \/>\r<br>Which strategy is correct for broadcasting?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='434420' \/><input type='hidden' id='answerType434420' 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-434420[]' id='answer-id-1680978' class='answer   answerof-434420 ' value='1680978'   \/><label for='answer-id-1680978' id='answer-label-1680978' class=' answer'><span>DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434420[]' id='answer-id-1680979' class='answer   answerof-434420 ' value='1680979'   \/><label for='answer-id-1680979' id='answer-label-1680979' class=' answer'><span>DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434420[]' id='answer-id-1680980' class='answer   answerof-434420 ' value='1680980'   \/><label for='answer-id-1680980' id='answer-label-1680980' class=' answer'><span>DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434420[]' id='answer-id-1680981' class='answer   answerof-434420 ' value='1680981'   \/><label for='answer-id-1680981' id='answer-label-1680981' class=' answer'><span>DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself<\/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-434421'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>Given the code fragment: <br \/>\r<br><br><img decoding=\"async\" width=597 height=44 id=\"\u56fe\u7247 42\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image010-7.jpg\"><br><br \/>\r<br>import pyspark.pandas as ps <br \/>\r<br>psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) <br \/>\r<br>Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='434421' \/><input type='hidden' id='answerType434421' 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-434421[]' id='answer-id-1680982' class='answer   answerof-434421 ' value='1680982'   \/><label for='answer-id-1680982' id='answer-label-1680982' class=' answer'><span>psdf.to_spark()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434421[]' id='answer-id-1680983' class='answer   answerof-434421 ' value='1680983'   \/><label for='answer-id-1680983' id='answer-label-1680983' class=' answer'><span>psdf.to_pyspark()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434421[]' id='answer-id-1680984' class='answer   answerof-434421 ' value='1680984'   \/><label for='answer-id-1680984' id='answer-label-1680984' class=' answer'><span>psdf.to_pandas()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434421[]' id='answer-id-1680985' class='answer   answerof-434421 ' value='1680985'   \/><label for='answer-id-1680985' id='answer-label-1680985' class=' answer'><span>psdf.to_dataframe()<\/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-434422'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='434422' \/><input type='hidden' id='answerType434422' 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-434422[]' id='answer-id-1680986' class='answer   answerof-434422 ' value='1680986'   \/><label for='answer-id-1680986' id='answer-label-1680986' class=' answer'><span>It provides a way to run Spark applications remotely in any programming language<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434422[]' id='answer-id-1680987' class='answer   answerof-434422 ' value='1680987'   \/><label for='answer-id-1680987' id='answer-label-1680987' class=' answer'><span>It can be used to interact with any remote cluster using the REST API<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434422[]' id='answer-id-1680988' class='answer   answerof-434422 ' value='1680988'   \/><label for='answer-id-1680988' id='answer-label-1680988' class=' answer'><span>It allows for remote execution of Spark jobs<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434422[]' id='answer-id-1680989' class='answer   answerof-434422 ' value='1680989'   \/><label for='answer-id-1680989' id='answer-label-1680989' class=' answer'><span>It is primarily used for data ingestion into Spark from external sources<\/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-434423'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold. <br \/>\r<br>Which type of join will Adaptive Query Execution (AQE) choose in this case?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='434423' \/><input type='hidden' id='answerType434423' 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-434423[]' id='answer-id-1680990' class='answer   answerof-434423 ' value='1680990'   \/><label for='answer-id-1680990' id='answer-label-1680990' class=' answer'><span>A Cartesian join<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434423[]' id='answer-id-1680991' class='answer   answerof-434423 ' value='1680991'   \/><label for='answer-id-1680991' id='answer-label-1680991' class=' answer'><span>A shuffled hash join<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434423[]' id='answer-id-1680992' class='answer   answerof-434423 ' value='1680992'   \/><label for='answer-id-1680992' id='answer-label-1680992' class=' answer'><span>A broadcast nested loop join<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434423[]' id='answer-id-1680993' class='answer   answerof-434423 ' value='1680993'   \/><label for='answer-id-1680993' id='answer-label-1680993' class=' answer'><span>A sort-merge join<\/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-434424'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>Given a DataFrame df that has 10 partitions, after running the code: <br \/>\r<br>result = df.coalesce(20) <br \/>\r<br>How many partitions will the result DataFrame have?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='434424' \/><input type='hidden' id='answerType434424' 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-434424[]' id='answer-id-1680994' class='answer   answerof-434424 ' value='1680994'   \/><label for='answer-id-1680994' id='answer-label-1680994' class=' answer'><span>10<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434424[]' id='answer-id-1680995' class='answer   answerof-434424 ' value='1680995'   \/><label for='answer-id-1680995' id='answer-label-1680995' class=' answer'><span>Same number as the cluster executors<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434424[]' id='answer-id-1680996' class='answer   answerof-434424 ' value='1680996'   \/><label for='answer-id-1680996' id='answer-label-1680996' class=' answer'><span>1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434424[]' id='answer-id-1680997' class='answer   answerof-434424 ' value='1680997'   \/><label for='answer-id-1680997' id='answer-label-1680997' class=' answer'><span>20<\/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-434425'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code: <br \/>\r<br><br><img decoding=\"async\" width=650 height=121 id=\"\u56fe\u7247 48\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image004-12.jpg\"><br><br \/>\r<br>fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) <br \/>\r<br>The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table. <br \/>\r<br>Which change should be made to the code to stop these customer records from being dropped?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='434425' \/><input type='hidden' id='answerType434425' 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-434425[]' id='answer-id-1680998' class='answer   answerof-434425 ' value='1680998'   \/><label for='answer-id-1680998' id='answer-label-1680998' class=' answer'><span>fact_df = purch_df.join(cust_df,<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1680999' class='answer   answerof-434425 ' value='1680999'   \/><label for='answer-id-1680999' id='answer-label-1680999' class=' answer'><span>col('customer_id') ==<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681000' class='answer   answerof-434425 ' value='1681000'   \/><label for='answer-id-1681000' id='answer-label-1681000' class=' answer'><span>col('custid'), 'left')<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681001' class='answer   answerof-434425 ' value='1681001'   \/><label for='answer-id-1681001' id='answer-label-1681001' class=' answer'><span>fact_df = cust_df.join(purch_df,<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681002' class='answer   answerof-434425 ' value='1681002'   \/><label for='answer-id-1681002' id='answer-label-1681002' class=' answer'><span>col('customer_id') ==<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681003' class='answer   answerof-434425 ' value='1681003'   \/><label for='answer-id-1681003' id='answer-label-1681003' class=' answer'><span>col('custid'))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681004' class='answer   answerof-434425 ' value='1681004'   \/><label for='answer-id-1681004' id='answer-label-1681004' class=' answer'><span>fact_df = purch_df.join(cust_df,<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681005' class='answer   answerof-434425 ' value='1681005'   \/><label for='answer-id-1681005' id='answer-label-1681005' class=' answer'><span>col('cust_id') ==<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681006' class='answer   answerof-434425 ' value='1681006'   \/><label for='answer-id-1681006' id='answer-label-1681006' class=' answer'><span>col('customer_id'))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681007' class='answer   answerof-434425 ' value='1681007'   \/><label for='answer-id-1681007' id='answer-label-1681007' class=' answer'><span>fact_df = purch_df.join(cust_df,<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681008' class='answer   answerof-434425 ' value='1681008'   \/><label for='answer-id-1681008' id='answer-label-1681008' class=' answer'><span>col('customer_id') ==<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434425[]' id='answer-id-1681009' class='answer   answerof-434425 ' value='1681009'   \/><label for='answer-id-1681009' id='answer-label-1681009' class=' answer'><span>col('custid'), 'right_outer')<\/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-434426'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field. <br \/>\r<br>Which line of Spark code will produce a Parquet table that meets these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='434426' \/><input type='hidden' id='answerType434426' 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-434426[]' id='answer-id-1681010' class='answer   answerof-434426 ' value='1681010'   \/><label for='answer-id-1681010' id='answer-label-1681010' class=' answer'><span>final_df  \r\n.sort(&quot;market_time&quot;)  \r\n.write  \r\n.format(&quot;parquet&quot;)  \r\n.mode(&quot;overwrite&quot;)  \r\n.saveAsTable(&quot;output.market_events&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434426[]' id='answer-id-1681011' class='answer   answerof-434426 ' value='1681011'   \/><label for='answer-id-1681011' id='answer-label-1681011' class=' answer'><span>final_df  \r\n.orderBy(&quot;market_time&quot;)  \r\n.write  \r\n.format(&quot;parquet&quot;)  \r\n.mode(&quot;overwrite&quot;)  \r\n.saveAsTable(&quot;output.market_events&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434426[]' id='answer-id-1681012' class='answer   answerof-434426 ' value='1681012'   \/><label for='answer-id-1681012' id='answer-label-1681012' class=' answer'><span>final_df  \r\n.sort(&quot;market_time&quot;)  \r\n.coalesce(1)  \r\n.write  \r\n.format(&quot;parquet&quot;)  \r\n.mode(&quot;overwrite&quot;)  \r\n.saveAsTable(&quot;output.market_events&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434426[]' id='answer-id-1681013' class='answer   answerof-434426 ' value='1681013'   \/><label for='answer-id-1681013' id='answer-label-1681013' class=' answer'><span>final_df  \r\n.sortWithinPartitions(&quot;market_time&quot;)  \r\n.write  \r\n.format(&quot;parquet&quot;)  \r\n.mode(&quot;overwrite&quot;)  \r\n.saveAsTable(&quot;output.market_events&quot;)<\/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-434427'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A data engineer writes the following code to join two DataFrames df1 and df2: <br \/>\r<br>df1 = spark.read.csv(&quot;sales_data.csv&quot;) # ~10 GB <br \/>\r<br>df2 = spark.read.csv(&quot;product_data.csv&quot;) # ~8 MB <br \/>\r<br>result = df1.join(df2, df1.product_id == df2.product_id) <br \/>\r<br><br><img decoding=\"async\" width=637 height=71 id=\"\u56fe\u7247 34\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image018-7.jpg\"><br><br \/>\r<br>Which join strategy will Spark use?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='434427' \/><input type='hidden' id='answerType434427' 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-434427[]' id='answer-id-1681014' class='answer   answerof-434427 ' value='1681014'   \/><label for='answer-id-1681014' id='answer-label-1681014' class=' answer'><span>Shuffle join, because AQE is not enabled, and Spark uses a static query plan<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434427[]' id='answer-id-1681015' class='answer   answerof-434427 ' value='1681015'   \/><label for='answer-id-1681015' id='answer-label-1681015' class=' answer'><span>Broadcast join, as df2 is smaller than the default broadcast threshold<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434427[]' id='answer-id-1681016' class='answer   answerof-434427 ' value='1681016'   \/><label for='answer-id-1681016' id='answer-label-1681016' class=' answer'><span>Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434427[]' id='answer-id-1681017' class='answer   answerof-434427 ' value='1681017'   \/><label for='answer-id-1681017' id='answer-label-1681017' class=' answer'><span>Shuffle join because no broadcast hints were provided<\/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-434428'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. <br \/>\r<br>The engineer adds: <br \/>\r<br>dropDuplicatesWithinWatermark(&quot;event_timestamp&quot;, &quot;30 minutes&quot;) <br \/>\r<br>What is the result?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='434428' \/><input type='hidden' id='answerType434428' 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-434428[]' id='answer-id-1681018' class='answer   answerof-434428 ' value='1681018'   \/><label for='answer-id-1681018' id='answer-label-1681018' class=' answer'><span>It is not able to handle deduplication in this scenario<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434428[]' id='answer-id-1681019' class='answer   answerof-434428 ' value='1681019'   \/><label for='answer-id-1681019' id='answer-label-1681019' class=' answer'><span>It removes duplicates that arrive within the 30-minute window specified by the watermark<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434428[]' id='answer-id-1681020' class='answer   answerof-434428 ' value='1681020'   \/><label for='answer-id-1681020' id='answer-label-1681020' class=' answer'><span>It removes all duplicates regardless of when they arrive<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434428[]' id='answer-id-1681021' class='answer   answerof-434428 ' value='1681021'   \/><label for='answer-id-1681021' id='answer-label-1681021' class=' answer'><span>It accepts watermarks in seconds and the code results in an error<\/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-434429'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results. <br \/>\r<br>How does Apache Spark&#8482;'s execution hierarchy process the operations when the data scientist runs this script?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='434429' \/><input type='hidden' id='answerType434429' 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-434429[]' id='answer-id-1681022' class='answer   answerof-434429 ' value='1681022'   \/><label for='answer-id-1681022' id='answer-label-1681022' class=' answer'><span>The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434429[]' id='answer-id-1681023' class='answer   answerof-434429 ' value='1681023'   \/><label for='answer-id-1681023' id='answer-label-1681023' class=' answer'><span>The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434429[]' id='answer-id-1681024' class='answer   answerof-434429 ' value='1681024'   \/><label for='answer-id-1681024' id='answer-label-1681024' class=' answer'><span>The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434429[]' id='answer-id-1681025' class='answer   answerof-434429 ' value='1681025'   \/><label for='answer-id-1681025' id='answer-label-1681025' class=' answer'><span>Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.<\/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-434430'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan. <br \/>\r<br>Which operation results in a shuffle and a new stage?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='434430' \/><input type='hidden' id='answerType434430' 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-434430[]' id='answer-id-1681026' class='answer   answerof-434430 ' value='1681026'   \/><label for='answer-id-1681026' id='answer-label-1681026' class=' answer'><span>DataFrame.groupBy().agg()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434430[]' id='answer-id-1681027' class='answer   answerof-434430 ' value='1681027'   \/><label for='answer-id-1681027' id='answer-label-1681027' class=' answer'><span>DataFrame.filter()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434430[]' id='answer-id-1681028' class='answer   answerof-434430 ' value='1681028'   \/><label for='answer-id-1681028' id='answer-label-1681028' class=' answer'><span>DataFrame.withColumn()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434430[]' id='answer-id-1681029' class='answer   answerof-434430 ' value='1681029'   \/><label for='answer-id-1681029' id='answer-label-1681029' class=' answer'><span>DataFrame.select()<\/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-434431'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory. <br \/>\r<br>What is the likely behavior when Spark runs out of memory to store the DataFrame?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='434431' \/><input type='hidden' id='answerType434431' 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-434431[]' id='answer-id-1681030' class='answer   answerof-434431 ' value='1681030'   \/><label for='answer-id-1681030' id='answer-label-1681030' class=' answer'><span>Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434431[]' id='answer-id-1681031' class='answer   answerof-434431 ' value='1681031'   \/><label for='answer-id-1681031' id='answer-label-1681031' class=' answer'><span>Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434431[]' id='answer-id-1681032' class='answer   answerof-434431 ' value='1681032'   \/><label for='answer-id-1681032' id='answer-label-1681032' class=' answer'><span>Spark will store as much data as possible in memory and spill the rest to disk when memory is full, \r\ncontinuing processing with performance overhead.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434431[]' id='answer-id-1681033' class='answer   answerof-434431 ' value='1681033'   \/><label for='answer-id-1681033' id='answer-label-1681033' class=' answer'><span>Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.<\/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-434432'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>1.A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.<br \/>\r\n<br \/>\r\nWhich code snippet can be used to meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='434432' \/><input type='hidden' id='answerType434432' 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-434432[]' id='answer-id-1681034' class='answer   answerof-434432 ' value='1681034'   \/><label for='answer-id-1681034' id='answer-label-1681034' class=' answer'><span>df_user_non_pii = df_user.drop(\"first_name\", \"last_name\", \"email\", \"birthdate\")<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434432[]' id='answer-id-1682502' class='answer   answerof-434432 ' value='1682502'   \/><label for='answer-id-1682502' id='answer-label-1682502' class=' answer'><span>df_user_non_pii = df_user.drop(\"first_name\", \"last_name\", \"email\", \"birthdate\")<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434432[]' id='answer-id-1682503' class='answer   answerof-434432 ' value='1682503'   \/><label for='answer-id-1682503' id='answer-label-1682503' class=' answer'><span>df_user_non_pii = df_user.dropfields(\"first_name\", \"last_name\", \"email\", \"birthdate\")<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434432[]' id='answer-id-1682504' class='answer   answerof-434432 ' value='1682504'   \/><label for='answer-id-1682504' id='answer-label-1682504' class=' answer'><span>df_user_non_pii = df_user.dropfields(\"first_name, last_name, email, birthdate\")<\/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-434433'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>What is the difference between df.cache() and df.persist() in Spark DataFrame?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='434433' \/><input type='hidden' id='answerType434433' 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-434433[]' id='answer-id-1681035' class='answer   answerof-434433 ' value='1681035'   \/><label for='answer-id-1681035' id='answer-label-1681035' class=' answer'><span>Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434433[]' id='answer-id-1681036' class='answer   answerof-434433 ' value='1681036'   \/><label for='answer-id-1681036' id='answer-label-1681036' class=' answer'><span>Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONL<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434433[]' id='answer-id-1681037' class='answer   answerof-434433 ' value='1681037'   \/><label for='answer-id-1681037' id='answer-label-1681037' class=' answer'><span>persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) and cache() - Can be used to set different storage levels to persist the contents of the DataFrame.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434433[]' id='answer-id-1681038' class='answer   answerof-434433 ' value='1681038'   \/><label for='answer-id-1681038' id='answer-label-1681038' class=' answer'><span>cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK) and persist() - Can be used to set different storage levels to persist the contents of the DataFrame<\/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-434434'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>In the code block below, aggDF contains aggregations on a streaming DataFrame: <br \/>\r<br><br><img decoding=\"async\" width=361 height=121 id=\"\u56fe\u7247 35\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image017-8.jpg\"><br><br \/>\r<br>Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='434434' \/><input type='hidden' id='answerType434434' 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-434434[]' id='answer-id-1681039' class='answer   answerof-434434 ' value='1681039'   \/><label for='answer-id-1681039' id='answer-label-1681039' class=' answer'><span>complete<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434434[]' id='answer-id-1681040' class='answer   answerof-434434 ' value='1681040'   \/><label for='answer-id-1681040' id='answer-label-1681040' class=' answer'><span>append<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434434[]' id='answer-id-1681041' class='answer   answerof-434434 ' value='1681041'   \/><label for='answer-id-1681041' id='answer-label-1681041' class=' answer'><span>replace<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434434[]' id='answer-id-1681042' class='answer   answerof-434434 ' value='1681042'   \/><label for='answer-id-1681042' id='answer-label-1681042' class=' answer'><span>aggregate<\/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-434435'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline. <br \/>\r<br>The initial code is: <br \/>\r<br><br><img decoding=\"async\" width=649 height=127 id=\"\u56fe\u7247 49\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image003-13.jpg\"><br><br \/>\r<br>def in_spanish_inner(df: pd.Series) -&gt; pd.Series: <br \/>\r<br>model = get_translation_model(target_lang='es') <br \/>\r<br>return df.apply(model) <br \/>\r<br>in_spanish = sf.pandas_udf(in_spanish_inner, StringType()) <br \/>\r<br>How can the MLOps engineer change this code to reduce how many times the language model is loaded?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='434435' \/><input type='hidden' id='answerType434435' 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-434435[]' id='answer-id-1681043' class='answer   answerof-434435 ' value='1681043'   \/><label for='answer-id-1681043' id='answer-label-1681043' class=' answer'><span>Convert the Pandas UDF to a PySpark UDF<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434435[]' id='answer-id-1681044' class='answer   answerof-434435 ' value='1681044'   \/><label for='answer-id-1681044' id='answer-label-1681044' class=' answer'><span>Convert the Pandas UDF from a Series \u2192 Series UDF to a Series \u2192 Scalar UDF<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434435[]' id='answer-id-1681045' class='answer   answerof-434435 ' value='1681045'   \/><label for='answer-id-1681045' id='answer-label-1681045' class=' answer'><span>Run the in_spanish_inner() function in a mapInPandas() function call<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434435[]' id='answer-id-1681046' class='answer   answerof-434435 ' value='1681046'   \/><label for='answer-id-1681046' id='answer-label-1681046' class=' answer'><span>Convert the Pandas UDF from a Series \u2192 Series UDF to an Iterator[Series] \u2192 Iterator[Series] UDF<\/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-434436'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>Which UDF implementation calculates the length of strings in a Spark DataFrame?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='434436' \/><input type='hidden' id='answerType434436' 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-434436[]' id='answer-id-1681047' class='answer   answerof-434436 ' value='1681047'   \/><label for='answer-id-1681047' id='answer-label-1681047' class=' answer'><span>df.withColumn(&quot;length&quot;, spark.udf(&quot;len&quot;, StringType()))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434436[]' id='answer-id-1681048' class='answer   answerof-434436 ' value='1681048'   \/><label for='answer-id-1681048' id='answer-label-1681048' class=' answer'><span>df.select(length(col(&quot;stringColumn&quot;)).alias(&quot;length&quot;))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434436[]' id='answer-id-1681049' class='answer   answerof-434436 ' value='1681049'   \/><label for='answer-id-1681049' id='answer-label-1681049' class=' answer'><span>spark.udf.register(&quot;stringLength&quot;, lambda s: len(s))<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434436[]' id='answer-id-1681050' class='answer   answerof-434436 ' value='1681050'   \/><label for='answer-id-1681050' id='answer-label-1681050' class=' answer'><span>df.withColumn(&quot;length&quot;, udf(lambda s: len(s), StringType()))<\/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-434437'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>Given the following code snippet in my_spark_app.py: <br \/>\r<br><br><img decoding=\"async\" width=649 height=258 id=\"\u56fe\u7247 40\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image012-8.jpg\"><br><br \/>\r<br>What is the role of the driver node?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='434437' \/><input type='hidden' id='answerType434437' 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-434437[]' id='answer-id-1681051' class='answer   answerof-434437 ' value='1681051'   \/><label for='answer-id-1681051' id='answer-label-1681051' class=' answer'><span>The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434437[]' id='answer-id-1681052' class='answer   answerof-434437 ' value='1681052'   \/><label for='answer-id-1681052' id='answer-label-1681052' class=' answer'><span>The driver node only provides the user interface for monitoring the application<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434437[]' id='answer-id-1681053' class='answer   answerof-434437 ' value='1681053'   \/><label for='answer-id-1681053' id='answer-label-1681053' class=' answer'><span>The driver node holds the DataFrame data and performs all computations locally<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434437[]' id='answer-id-1681054' class='answer   answerof-434437 ' value='1681054'   \/><label for='answer-id-1681054' id='answer-label-1681054' class=' answer'><span>The driver node stores the final result after computations are completed by worker nodes<\/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-434438'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order. <br \/>\r<br>Which code snippet meets the requirement of the developer?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='434438' \/><input type='hidden' id='answerType434438' 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-434438[]' id='answer-id-1681055' class='answer   answerof-434438 ' value='1681055'   \/><label for='answer-id-1681055' id='answer-label-1681055' class=' answer'><span>df.orderBy(col(&quot;age&quot;).asc(), col(&quot;salary&quot;).asc()).show()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434438[]' id='answer-id-1681056' class='answer   answerof-434438 ' value='1681056'   \/><label for='answer-id-1681056' id='answer-label-1681056' class=' answer'><span>df.sort(&quot;age&quot;, &quot;salary&quot;, ascending=[True, True]).show()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434438[]' id='answer-id-1681057' class='answer   answerof-434438 ' value='1681057'   \/><label for='answer-id-1681057' id='answer-label-1681057' class=' answer'><span>df.sort(&quot;age&quot;, &quot;salary&quot;, ascending=[False, True]).show()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434438[]' id='answer-id-1681058' class='answer   answerof-434438 ' value='1681058'   \/><label for='answer-id-1681058' id='answer-label-1681058' class=' answer'><span>df.orderBy(&quot;age&quot;, &quot;salary&quot;, ascending=[True, False]).show()<\/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-434439'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values. <br \/>\r<br>Which change should be made to solve the issue? <br \/>\r<br><br><img decoding=\"async\" width=649 height=25 id=\"\u56fe\u7247 23\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image029-4.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_20' value='434439' \/><input type='hidden' id='answerType434439' 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-434439[]' id='answer-id-1681059' class='answer   answerof-434439 ' value='1681059'   \/><label for='answer-id-1681059' id='answer-label-1681059' class=' answer'><span>Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434439[]' id='answer-id-1681060' class='answer   answerof-434439 ' value='1681060'   \/><label for='answer-id-1681060' id='answer-label-1681060' class=' answer'><span>Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434439[]' id='answer-id-1681061' class='answer   answerof-434439 ' value='1681061'   \/><label for='answer-id-1681061' id='answer-label-1681061' class=' answer'><span>Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434439[]' id='answer-id-1681062' class='answer   answerof-434439 ' value='1681062'   \/><label for='answer-id-1681062' id='answer-label-1681062' class=' answer'><span>Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy<\/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-434440'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='434440' \/><input type='hidden' id='answerType434440' 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-434440[]' id='answer-id-1681063' class='answer   answerof-434440 ' value='1681063'   \/><label for='answer-id-1681063' id='answer-label-1681063' class=' answer'><span>The same start date will be returned<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434440[]' id='answer-id-1681064' class='answer   answerof-434440 ' value='1681064'   \/><label for='answer-id-1681064' id='answer-label-1681064' class=' answer'><span>An error message of an invalid parameter will be returned<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434440[]' id='answer-id-1681065' class='answer   answerof-434440 ' value='1681065'   \/><label for='answer-id-1681065' id='answer-label-1681065' class=' answer'><span>The number of days specified will be added to the start date<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434440[]' id='answer-id-1681066' class='answer   answerof-434440 ' value='1681066'   \/><label for='answer-id-1681066' id='answer-label-1681066' class=' answer'><span>The number of days specified will be removed from the start date<\/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-434441'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off. <br \/>\r<br>How can this be achieved?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='434441' \/><input type='hidden' id='answerType434441' 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-434441[]' id='answer-id-1681067' class='answer   answerof-434441 ' value='1681067'   \/><label for='answer-id-1681067' id='answer-label-1681067' class=' answer'><span>By configuring the option checkpointLocation during readStream<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434441[]' id='answer-id-1681068' class='answer   answerof-434441 ' value='1681068'   \/><label for='answer-id-1681068' id='answer-label-1681068' class=' answer'><span>By configuring the option recoveryLocation during the SparkSession initialization<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434441[]' id='answer-id-1681069' class='answer   answerof-434441 ' value='1681069'   \/><label for='answer-id-1681069' id='answer-label-1681069' class=' answer'><span>By configuring the option recoveryLocation during writeStream<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434441[]' id='answer-id-1681070' class='answer   answerof-434441 ' value='1681070'   \/><label for='answer-id-1681070' id='answer-label-1681070' class=' answer'><span>By configuring the option checkpointLocation during writeStream<\/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-434442'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='434442' \/><input type='hidden' id='answerType434442' 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-434442[]' id='answer-id-1681071' class='answer   answerof-434442 ' value='1681071'   \/><label for='answer-id-1681071' id='answer-label-1681071' class=' answer'><span>The conversion will automatically distribute the data across worker nodes<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434442[]' id='answer-id-1681072' class='answer   answerof-434442 ' value='1681072'   \/><label for='answer-id-1681072' id='answer-label-1681072' class=' answer'><span>The operation will fail if the Pandas DataFrame exceeds 1000 rows<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434442[]' id='answer-id-1681073' class='answer   answerof-434442 ' value='1681073'   \/><label for='answer-id-1681073' id='answer-label-1681073' class=' answer'><span>Data will be lost during conversion<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434442[]' id='answer-id-1681074' class='answer   answerof-434442 ' value='1681074'   \/><label for='answer-id-1681074' id='answer-label-1681074' class=' answer'><span>The operation will load all data into the driver's memory, potentially causing memory overflow<\/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-434443'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A data engineer needs to write a Streaming DataFrame as Parquet files. <br \/>\r<br>Given the code: <br \/>\r<br><br><img decoding=\"async\" width=513 height=120 id=\"\u56fe\u7247 28\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image024-5.jpg\"><br><br \/>\r<br>Which code fragment should be inserted to meet the requirement? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=520 height=43 id=\"\u56fe\u7247 27\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image025-6.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=555 height=44 id=\"\u56fe\u7247 26\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image026-6.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=373 height=45 id=\"\u56fe\u7247 25\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image027-5.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=373 height=45 id=\"\u56fe\u7247 24\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image028-4.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_24' value='434443' \/><input type='hidden' id='answerType434443' 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-434443[]' id='answer-id-1681075' class='answer   answerof-434443 ' value='1681075'   \/><label for='answer-id-1681075' id='answer-label-1681075' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434443[]' id='answer-id-1681076' class='answer   answerof-434443 ' value='1681076'   \/><label for='answer-id-1681076' id='answer-label-1681076' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434443[]' id='answer-id-1681077' class='answer   answerof-434443 ' value='1681077'   \/><label for='answer-id-1681077' id='answer-label-1681077' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434443[]' id='answer-id-1681078' class='answer   answerof-434443 ' value='1681078'   \/><label for='answer-id-1681078' id='answer-label-1681078' class=' answer'><span>Option D<\/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-434444'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. <br \/>\r<br>Spark UI shows: <br \/>\r<br>Low number of Active Tasks <br \/>\r<br>Many tasks complete in milliseconds <br \/>\r<br>Fewer tasks than available CPUs <br \/>\r<br>Which approach should be used to adjust the partitioning for optimal resource allocation?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='434444' \/><input type='hidden' id='answerType434444' 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-434444[]' id='answer-id-1681079' class='answer   answerof-434444 ' value='1681079'   \/><label for='answer-id-1681079' id='answer-label-1681079' class=' answer'><span>Set the number of partitions equal to the total number of CPUs in the cluster<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434444[]' id='answer-id-1681080' class='answer   answerof-434444 ' value='1681080'   \/><label for='answer-id-1681080' id='answer-label-1681080' class=' answer'><span>Set the number of partitions to a fixed value, such as 200<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434444[]' id='answer-id-1681081' class='answer   answerof-434444 ' value='1681081'   \/><label for='answer-id-1681081' id='answer-label-1681081' class=' answer'><span>Set the number of partitions equal to the number of nodes in the cluster<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434444[]' id='answer-id-1681082' class='answer   answerof-434444 ' value='1681082'   \/><label for='answer-id-1681082' id='answer-label-1681082' class=' answer'><span>Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB<\/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-434445'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low. <br \/>\r<br>What should the developer do to improve cluster utilization?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='434445' \/><input type='hidden' id='answerType434445' 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-434445[]' id='answer-id-1681083' class='answer   answerof-434445 ' value='1681083'   \/><label for='answer-id-1681083' id='answer-label-1681083' class=' answer'><span>Increase the value of spark.sql.shuffle.partitions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434445[]' id='answer-id-1681084' class='answer   answerof-434445 ' value='1681084'   \/><label for='answer-id-1681084' id='answer-label-1681084' class=' answer'><span>Reduce the value of spark.sql.shuffle.partitions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434445[]' id='answer-id-1681085' class='answer   answerof-434445 ' value='1681085'   \/><label for='answer-id-1681085' id='answer-label-1681085' class=' answer'><span>Increase the size of the dataset to create more partitions<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434445[]' id='answer-id-1681086' class='answer   answerof-434445 ' value='1681086'   \/><label for='answer-id-1681086' id='answer-label-1681086' class=' answer'><span>Enable dynamic resource allocation to scale resources as needed<\/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-434446'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. <br \/>\r<br>What will be the outcome?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='434446' \/><input type='hidden' id='answerType434446' 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-434446[]' id='answer-id-1681087' class='answer   answerof-434446 ' value='1681087'   \/><label for='answer-id-1681087' id='answer-label-1681087' class=' answer'><span>The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434446[]' id='answer-id-1681088' class='answer   answerof-434446 ' value='1681088'   \/><label for='answer-id-1681088' id='answer-label-1681088' class=' answer'><span>The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434446[]' id='answer-id-1681089' class='answer   answerof-434446 ' value='1681089'   \/><label for='answer-id-1681089' id='answer-label-1681089' class=' answer'><span>The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434446[]' id='answer-id-1681090' class='answer   answerof-434446 ' value='1681090'   \/><label for='answer-id-1681090' id='answer-label-1681090' class=' answer'><span>The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame<\/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-434447'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>Given a CSV file with the content: <br \/>\r<br><br><img decoding=\"async\" width=649 height=130 id=\"\u56fe\u7247 33\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image019-7.jpg\"><br><br \/>\r<br>And the following code: <br \/>\r<br>from pyspark.sql.types import * <br \/>\r<br>schema = StructType([ <br \/>\r<br>StructField(&quot;name&quot;, StringType()), <br \/>\r<br>StructField(&quot;age&quot;, IntegerType()) <br \/>\r<br>]) <br \/>\r<br>spark.read.schema(schema).csv(path).collect() <br \/>\r<br>What is the resulting output?<\/div><input type='hidden' name='question_id[]' id='qID_28' value='434447' \/><input type='hidden' id='answerType434447' 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-434447[]' id='answer-id-1681091' class='answer   answerof-434447 ' value='1681091'   \/><label for='answer-id-1681091' id='answer-label-1681091' class=' answer'><span>[Row(name='bambi'), Row(name='alladin', age=20)]<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434447[]' id='answer-id-1681092' class='answer   answerof-434447 ' value='1681092'   \/><label for='answer-id-1681092' id='answer-label-1681092' class=' answer'><span>[Row(name='alladin', age=20)]<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434447[]' id='answer-id-1681093' class='answer   answerof-434447 ' value='1681093'   \/><label for='answer-id-1681093' id='answer-label-1681093' class=' answer'><span>[Row(name='bambi', age=None), Row(name='alladin', age=20)]<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434447[]' id='answer-id-1681094' class='answer   answerof-434447 ' value='1681094'   \/><label for='answer-id-1681094' id='answer-label-1681094' class=' answer'><span>The code throws an error due to a schema mismatch.<\/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-434448'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A developer is working with a pandas DataFrame containing user behavior data from a web application. <br \/>\r<br>Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5? <br \/>\r<br>A) Use the applylnPandas API <br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=649 height=97 id=\"\u56fe\u7247 38\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image014-9.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=649 height=106 id=\"\u56fe\u7247 37\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image015-9.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=640 height=238 id=\"\u56fe\u7247 36\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image016-7.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_29' value='434448' \/><input type='hidden' id='answerType434448' 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-434448[]' id='answer-id-1681095' class='answer   answerof-434448 ' value='1681095'   \/><label for='answer-id-1681095' id='answer-label-1681095' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434448[]' id='answer-id-1681096' class='answer   answerof-434448 ' value='1681096'   \/><label for='answer-id-1681096' id='answer-label-1681096' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434448[]' id='answer-id-1681097' class='answer   answerof-434448 ' value='1681097'   \/><label for='answer-id-1681097' id='answer-label-1681097' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434448[]' id='answer-id-1681098' class='answer   answerof-434448 ' value='1681098'   \/><label for='answer-id-1681098' id='answer-label-1681098' class=' answer'><span>Option D<\/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-434449'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis. <br \/>\r<br>Which technique should be used?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='434449' \/><input type='hidden' id='answerType434449' 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-434449[]' id='answer-id-1681099' class='answer   answerof-434449 ' value='1681099'   \/><label for='answer-id-1681099' id='answer-label-1681099' class=' answer'><span>Use an RDD action like reduce() to compute the maximum time<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434449[]' id='answer-id-1681100' class='answer   answerof-434449 ' value='1681100'   \/><label for='answer-id-1681100' id='answer-label-1681100' class=' answer'><span>Use an accumulator to record the maximum time on the driver<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434449[]' id='answer-id-1681101' class='answer   answerof-434449 ' value='1681101'   \/><label for='answer-id-1681101' id='answer-label-1681101' class=' answer'><span>Broadcast a variable to share the maximum time among workers<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434449[]' id='answer-id-1681102' class='answer   answerof-434449 ' value='1681102'   \/><label for='answer-id-1681102' id='answer-label-1681102' class=' answer'><span>Configure the Spark UI to automatically collect maximum times<\/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-434450'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A data engineer is running a batch processing job on a Spark cluster with the following configuration: <br \/>\r<br>10 worker nodes <br \/>\r<br>16 CPU cores per worker node <br \/>\r<br>64 GB RAM per node <br \/>\r<br>The data engineer wants to allocate four executors per node, each executor using four cores. <br \/>\r<br>What is the total number of CPU cores used by the application?<\/div><input type='hidden' name='question_id[]' id='qID_31' value='434450' \/><input type='hidden' id='answerType434450' 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-434450[]' id='answer-id-1681103' class='answer   answerof-434450 ' value='1681103'   \/><label for='answer-id-1681103' id='answer-label-1681103' class=' answer'><span>160<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434450[]' id='answer-id-1681104' class='answer   answerof-434450 ' value='1681104'   \/><label for='answer-id-1681104' id='answer-label-1681104' class=' answer'><span>64<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434450[]' id='answer-id-1681105' class='answer   answerof-434450 ' value='1681105'   \/><label for='answer-id-1681105' id='answer-label-1681105' class=' answer'><span>80<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434450[]' id='answer-id-1681106' class='answer   answerof-434450 ' value='1681106'   \/><label for='answer-id-1681106' id='answer-label-1681106' class=' answer'><span>40<\/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-434451'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of &quot;\/path\/events\/data&quot;. The upstream team drops daily data into the underlying subdirectories following the convention year\/month\/day. <br \/>\r<br>A few examples of the directory structure are: <br \/>\r<br><br><img decoding=\"async\" width=318 height=150 id=\"\u56fe\u7247 41\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image011-8.jpg\"><br><br \/>\r<br>Which of the following code snippets will read all the data within the directory structure?<\/div><input type='hidden' name='question_id[]' id='qID_32' value='434451' \/><input type='hidden' id='answerType434451' 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-434451[]' id='answer-id-1681107' class='answer   answerof-434451 ' value='1681107'   \/><label for='answer-id-1681107' id='answer-label-1681107' class=' answer'><span>df = spark.read.option(&quot;inferSchema&quot;, &quot;true&quot;).parquet(&quot;\/path\/events\/data\/&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434451[]' id='answer-id-1681108' class='answer   answerof-434451 ' value='1681108'   \/><label for='answer-id-1681108' id='answer-label-1681108' class=' answer'><span>df = spark.read.option(&quot;recursiveFileLookup&quot;, &quot;true&quot;).parquet(&quot;\/path\/events\/data\/&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434451[]' id='answer-id-1681109' class='answer   answerof-434451 ' value='1681109'   \/><label for='answer-id-1681109' id='answer-label-1681109' class=' answer'><span>df = spark.read.parquet(&quot;\/path\/events\/data\/*&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434451[]' id='answer-id-1681110' class='answer   answerof-434451 ' value='1681110'   \/><label for='answer-id-1681110' id='answer-label-1681110' class=' answer'><span>df = spark.read.parquet(&quot;\/path\/events\/data\/&quot;)<\/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-434452'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>An engineer has a large ORC file located at \/file\/test_data.orc and wants to read only specific columns to reduce memory usage. <br \/>\r<br>Which code fragment will select the columns, i.e., col1, col2, during the reading process?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='434452' \/><input type='hidden' id='answerType434452' 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-434452[]' id='answer-id-1681111' class='answer   answerof-434452 ' value='1681111'   \/><label for='answer-id-1681111' id='answer-label-1681111' class=' answer'><span>spark.read.orc(&quot;\/file\/test_data.orc&quot;).filter(&quot;col1 = 'value' &quot;).select(&quot;col2&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434452[]' id='answer-id-1681112' class='answer   answerof-434452 ' value='1681112'   \/><label for='answer-id-1681112' id='answer-label-1681112' class=' answer'><span>spark.read.format(&quot;orc&quot;).select(&quot;col1&quot;, &quot;col2&quot;).load(&quot;\/file\/test_data.orc&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434452[]' id='answer-id-1681113' class='answer   answerof-434452 ' value='1681113'   \/><label for='answer-id-1681113' id='answer-label-1681113' class=' answer'><span>spark.read.orc(&quot;\/file\/test_data.orc&quot;).selected(&quot;col1&quot;, &quot;col2&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434452[]' id='answer-id-1681114' class='answer   answerof-434452 ' value='1681114'   \/><label for='answer-id-1681114' id='answer-label-1681114' class=' answer'><span>spark.read.format(&quot;orc&quot;).load(&quot;\/file\/test_data.orc&quot;).select(&quot;col1&quot;, &quot;col2&quot;)<\/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-434453'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns: <br \/>\r<br>df1: employee_id INT, name STRING <br \/>\r<br>df2: emp_id INT, department STRING <br \/>\r<br>The engineer uses: <br \/>\r<br>result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner') <br \/>\r<br>What is the behaviour of the code snippet?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='434453' \/><input type='hidden' id='answerType434453' 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-434453[]' id='answer-id-1681115' class='answer   answerof-434453 ' value='1681115'   \/><label for='answer-id-1681115' id='answer-label-1681115' class=' answer'><span>The code fails to execute because the column names employee_id and emp_id do not match automatically<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434453[]' id='answer-id-1681116' class='answer   answerof-434453 ' value='1681116'   \/><label for='answer-id-1681116' id='answer-label-1681116' class=' answer'><span>The code fails to execute because it must use on='employee_id' to specify the join column explicitly<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434453[]' id='answer-id-1681117' class='answer   answerof-434453 ' value='1681117'   \/><label for='answer-id-1681117' id='answer-label-1681117' class=' answer'><span>The code fails to execute because PySpark does not support joining DataFrames with a different structure<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434453[]' id='answer-id-1681118' class='answer   answerof-434453 ' value='1681118'   \/><label for='answer-id-1681118' id='answer-label-1681118' class=' answer'><span>The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2<\/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-434454'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately. <br \/>\r<br>Which two characteristics of Apache Spark's execution model explain this behavior? Choose 2 answers:<\/div><input type='hidden' name='question_id[]' id='qID_35' value='434454' \/><input type='hidden' id='answerType434454' 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-434454[]' id='answer-id-1681119' class='answer   answerof-434454 ' value='1681119'   \/><label for='answer-id-1681119' id='answer-label-1681119' class=' answer'><span>The Spark engine requires manual intervention to start executing transformations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434454[]' id='answer-id-1681120' class='answer   answerof-434454 ' value='1681120'   \/><label for='answer-id-1681120' id='answer-label-1681120' class=' answer'><span>Only actions trigger the execution of the transformation pipeline.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434454[]' id='answer-id-1681121' class='answer   answerof-434454 ' value='1681121'   \/><label for='answer-id-1681121' id='answer-label-1681121' class=' answer'><span>Transformations are executed immediately to build the lineage graph.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434454[]' id='answer-id-1681122' class='answer   answerof-434454 ' value='1681122'   \/><label for='answer-id-1681122' id='answer-label-1681122' class=' answer'><span>The Spark engine optimizes the execution plan during the transformations, causing delays.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434454[]' id='answer-id-1681123' class='answer   answerof-434454 ' value='1681123'   \/><label for='answer-id-1681123' id='answer-label-1681123' class=' answer'><span>Transformations are evaluated lazily.<\/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-434455'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A Data Analyst is working on the DataFrame sensor_df, which contains two columns: <br \/>\r<br>Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=649 height=65 id=\"\u56fe\u7247 32\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image020-6.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=649 height=52 id=\"\u56fe\u7247 31\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image021-5.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=649 height=51 id=\"\u56fe\u7247 30\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image022-6.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=649 height=15 id=\"\u56fe\u7247 29\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image023-5.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_36' value='434455' \/><input type='hidden' id='answerType434455' 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-434455[]' id='answer-id-1681124' class='answer   answerof-434455 ' value='1681124'   \/><label for='answer-id-1681124' id='answer-label-1681124' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434455[]' id='answer-id-1681125' class='answer   answerof-434455 ' value='1681125'   \/><label for='answer-id-1681125' id='answer-label-1681125' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434455[]' id='answer-id-1681126' class='answer   answerof-434455 ' value='1681126'   \/><label for='answer-id-1681126' id='answer-label-1681126' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434455[]' id='answer-id-1681127' class='answer   answerof-434455 ' value='1681127'   \/><label for='answer-id-1681127' id='answer-label-1681127' class=' answer'><span>Option D<\/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-434456'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"\u56fe\u7247 50\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image002-11.jpg\" width=\"494\" height=\"247\" \/>Which operation is supported with streamingdf?<\/div><input type='hidden' name='question_id[]' id='qID_37' value='434456' \/><input type='hidden' id='answerType434456' 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-434456[]' id='answer-id-1681128' class='answer   answerof-434456 ' value='1681128'   \/><label for='answer-id-1681128' id='answer-label-1681128' class=' answer'><span>streaming_df. select (countDistinct (\"Name\") )\r\n<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434456[]' id='answer-id-1682505' class='answer   answerof-434456 ' value='1682505'   \/><label for='answer-id-1682505' id='answer-label-1682505' class=' answer'><span>streaming_df.groupby(\"Id\") .count ()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434456[]' id='answer-id-1682506' class='answer   answerof-434456 ' value='1682506'   \/><label for='answer-id-1682506' id='answer-label-1682506' class=' answer'><span>streaming_df.orderBy(\"timestamp\").limit(4)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434456[]' id='answer-id-1682507' class='answer   answerof-434456 ' value='1682507'   \/><label for='answer-id-1682507' id='answer-label-1682507' class=' answer'><span>streaming_df.filter (col(\"count\") < 30).show()<\/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-434457'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce. <br \/>\r<br>Which operation results in a shuffle?<\/div><input type='hidden' name='question_id[]' id='qID_38' value='434457' \/><input type='hidden' id='answerType434457' 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-434457[]' id='answer-id-1681129' class='answer   answerof-434457 ' value='1681129'   \/><label for='answer-id-1681129' id='answer-label-1681129' class=' answer'><span>groupBy<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434457[]' id='answer-id-1681130' class='answer   answerof-434457 ' value='1681130'   \/><label for='answer-id-1681130' id='answer-label-1681130' class=' answer'><span>filter<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434457[]' id='answer-id-1681131' class='answer   answerof-434457 ' value='1681131'   \/><label for='answer-id-1681131' id='answer-label-1681131' class=' answer'><span>select<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434457[]' id='answer-id-1681132' class='answer   answerof-434457 ' value='1681132'   \/><label for='answer-id-1681132' id='answer-label-1681132' class=' answer'><span>coalesce<\/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-434458'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this: <br \/>\r<br><br><img decoding=\"async\" width=258 height=224 id=\"\u56fe\u7247 47\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image005-10.jpg\"><br><br \/>\r<br>The resulting Python dictionary must contain a mapping of region -&gt; region id containing the smallest 3 region_id values. <br \/>\r<br>Which code fragment meets the requirements? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=404 height=150 id=\"\u56fe\u7247 46\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image006-10.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=401 height=176 id=\"\u56fe\u7247 45\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image007-11.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=405 height=151 id=\"\u56fe\u7247 44\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image008-10.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=405 height=149 id=\"\u56fe\u7247 43\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image009-9.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_39' value='434458' \/><input type='hidden' id='answerType434458' 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-434458[]' id='answer-id-1681133' class='answer   answerof-434458 ' value='1681133'   \/><label for='answer-id-1681133' id='answer-label-1681133' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434458[]' id='answer-id-1681134' class='answer   answerof-434458 ' value='1681134'   \/><label for='answer-id-1681134' id='answer-label-1681134' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434458[]' id='answer-id-1681135' class='answer   answerof-434458 ' value='1681135'   \/><label for='answer-id-1681135' id='answer-label-1681135' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434458[]' id='answer-id-1681136' class='answer   answerof-434458 ' value='1681136'   \/><label for='answer-id-1681136' id='answer-label-1681136' class=' answer'><span>Option D<\/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-434459'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything. <br \/>\r<br>Which save mode and method should be used?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='434459' \/><input type='hidden' id='answerType434459' 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-434459[]' id='answer-id-1681137' class='answer   answerof-434459 ' value='1681137'   \/><label for='answer-id-1681137' id='answer-label-1681137' class=' answer'><span>saveAsTable with mode ErrorIfExists<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434459[]' id='answer-id-1681138' class='answer   answerof-434459 ' value='1681138'   \/><label for='answer-id-1681138' id='answer-label-1681138' class=' answer'><span>saveAsTable with mode Overwrite<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434459[]' id='answer-id-1681139' class='answer   answerof-434459 ' value='1681139'   \/><label for='answer-id-1681139' id='answer-label-1681139' class=' answer'><span>save with mode Ignore<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434459[]' id='answer-id-1681140' class='answer   answerof-434459 ' value='1681140'   \/><label for='answer-id-1681140' id='answer-label-1681140' class=' answer'><span>save with mode ErrorIfExists<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-41' style=';'><div id='questionWrap-41'  class='   watupro-question-id-434460'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>41. <\/span>Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?<\/div><input type='hidden' name='question_id[]' id='qID_41' value='434460' \/><input type='hidden' id='answerType434460' 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-434460[]' id='answer-id-1681141' class='answer   answerof-434460 ' value='1681141'   \/><label for='answer-id-1681141' id='answer-label-1681141' class=' answer'><span>spark.conf.set(&quot;spark.pandas.arrow.enabled&quot;, &quot;true&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434460[]' id='answer-id-1681142' class='answer   answerof-434460 ' value='1681142'   \/><label for='answer-id-1681142' id='answer-label-1681142' class=' answer'><span>spark.conf.set(&quot;spark.sql.execution.arrow.pyspark.enabled&quot;, &quot;true&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434460[]' id='answer-id-1681143' class='answer   answerof-434460 ' value='1681143'   \/><label for='answer-id-1681143' id='answer-label-1681143' class=' answer'><span>spark.conf.set(&quot;spark.sql.execution.arrow.enabled&quot;, &quot;true&quot;)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434460[]' id='answer-id-1681144' class='answer   answerof-434460 ' value='1681144'   \/><label for='answer-id-1681144' id='answer-label-1681144' class=' answer'><span>spark.conf.set(&quot;spark.sql.arrow.pandas.enabled&quot;, &quot;true&quot;)<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-42' style=';'><div id='questionWrap-42'  class='   watupro-question-id-434461'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>42. <\/span>A data engineer is streaming data from Kafka and requires: <br \/>\r<br>Minimal latency <br \/>\r<br>Exactly-once processing guarantees <br \/>\r<br>Which trigger mode should be used?<\/div><input type='hidden' name='question_id[]' id='qID_42' value='434461' \/><input type='hidden' id='answerType434461' 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-434461[]' id='answer-id-1681145' class='answer   answerof-434461 ' value='1681145'   \/><label for='answer-id-1681145' id='answer-label-1681145' class=' answer'><span>.trigger(processingTime='1 second')<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434461[]' id='answer-id-1681146' class='answer   answerof-434461 ' value='1681146'   \/><label for='answer-id-1681146' id='answer-label-1681146' class=' answer'><span>.trigger(continuous=True)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434461[]' id='answer-id-1681147' class='answer   answerof-434461 ' value='1681147'   \/><label for='answer-id-1681147' id='answer-label-1681147' class=' answer'><span>.trigger(continuous='1 second')<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434461[]' id='answer-id-1681148' class='answer   answerof-434461 ' value='1681148'   \/><label for='answer-id-1681148' id='answer-label-1681148' class=' answer'><span>.trigger(availableNow=True)<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-43' style=';'><div id='questionWrap-43'  class='   watupro-question-id-434462'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>43. <\/span>A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. <br \/>\r<br>The existing UDF code is: <br \/>\r<br><br><img decoding=\"async\" width=635 height=227 id=\"\u56fe\u7247 39\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/10\/image013-8.jpg\"><br><br \/>\r<br>import hashlib <br \/>\r<br>import pyspark.sql.functions as sf <br \/>\r<br>from pyspark.sql.types import StringType <br \/>\r<br>def shake_256(raw): <br \/>\r<br>return hashlib.shake_256(raw.encode()).hexdigest(20) <br \/>\r<br>shake_256_udf = sf.udf(shake_256, StringType()) <br \/>\r<br>The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit <br \/>\r<br>shake_256_udf = sf.pandas_udf(shake_256, StringType()) <br \/>\r<br>However, the developer receives the error: <br \/>\r<br>What should the signature of the shake_256() function be changed to in order to fix this error?<\/div><input type='hidden' name='question_id[]' id='qID_43' value='434462' \/><input type='hidden' id='answerType434462' 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-434462[]' id='answer-id-1681149' class='answer   answerof-434462 ' value='1681149'   \/><label for='answer-id-1681149' id='answer-label-1681149' class=' answer'><span>def shake_256(df: pd.Series) -&gt; str:<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434462[]' id='answer-id-1681150' class='answer   answerof-434462 ' value='1681150'   \/><label for='answer-id-1681150' id='answer-label-1681150' class=' answer'><span>def shake_256(df: Iterator[pd.Series]) -&gt; Iterator[pd.Series]:<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434462[]' id='answer-id-1681151' class='answer   answerof-434462 ' value='1681151'   \/><label for='answer-id-1681151' id='answer-label-1681151' class=' answer'><span>def shake_256(raw: str) -&gt; str:<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434462[]' id='answer-id-1681152' class='answer   answerof-434462 ' value='1681152'   \/><label for='answer-id-1681152' id='answer-label-1681152' class=' answer'><span>def shake_256(df: pd.Series) -&gt; pd.Series:<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-44' style=';'><div id='questionWrap-44'  class='   watupro-question-id-434463'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>44. <\/span>A Spark application is experiencing performance issues in client mode because the driver is resource-constrained. <br \/>\r<br>How should this issue be resolved?<\/div><input type='hidden' name='question_id[]' id='qID_44' value='434463' \/><input type='hidden' id='answerType434463' 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-434463[]' id='answer-id-1681153' class='answer   answerof-434463 ' value='1681153'   \/><label for='answer-id-1681153' id='answer-label-1681153' class=' answer'><span>Add more executor instances to the cluster<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434463[]' id='answer-id-1681154' class='answer   answerof-434463 ' value='1681154'   \/><label for='answer-id-1681154' id='answer-label-1681154' class=' answer'><span>Increase the driver memory on the client machine<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434463[]' id='answer-id-1681155' class='answer   answerof-434463 ' value='1681155'   \/><label for='answer-id-1681155' id='answer-label-1681155' class=' answer'><span>Switch the deployment mode to cluster mode<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434463[]' id='answer-id-1681156' class='answer   answerof-434463 ' value='1681156'   \/><label for='answer-id-1681156' id='answer-label-1681156' class=' answer'><span>Switch the deployment mode to local mode<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-45' style=';'><div id='questionWrap-45'  class='   watupro-question-id-434464'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>45. <\/span>A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple &quot;GC overhead limit exceeded&quot; messages. <br \/>\r<br>Which action should the engineer take to resolve this issue?<\/div><input type='hidden' name='question_id[]' id='qID_45' value='434464' \/><input type='hidden' id='answerType434464' 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-434464[]' id='answer-id-1681157' class='answer   answerof-434464 ' value='1681157'   \/><label for='answer-id-1681157' id='answer-label-1681157' class=' answer'><span>Optimize the data processing logic by repartitioning the DataFrame.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434464[]' id='answer-id-1681158' class='answer   answerof-434464 ' value='1681158'   \/><label for='answer-id-1681158' id='answer-label-1681158' class=' answer'><span>Modify the Spark configuration to disable garbage collection<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434464[]' id='answer-id-1681159' class='answer   answerof-434464 ' value='1681159'   \/><label for='answer-id-1681159' id='answer-label-1681159' class=' answer'><span>Increase the memory allocated to the Spark Driver.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434464[]' id='answer-id-1681160' class='answer   answerof-434464 ' value='1681160'   \/><label for='answer-id-1681160' id='answer-label-1681160' class=' answer'><span>Cache large DataFrames to persist them in memory.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-46' style=';'><div id='questionWrap-46'  class='   watupro-question-id-434465'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>46. <\/span>A data engineer is building an Apache Spark&#8482; Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints. <br \/>\r<br>Which code snippet should the data engineer use?<\/div><input type='hidden' name='question_id[]' id='qID_46' value='434465' \/><input type='hidden' id='answerType434465' 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-434465[]' id='answer-id-1681161' class='answer   answerof-434465 ' value='1681161'   \/><label for='answer-id-1681161' id='answer-label-1681161' class=' answer'><span>query = streaming_df.writeStream  \r\n.format(&quot;console&quot;)  \r\n.option(&quot;checkpoint&quot;, &quot;\/path\/to\/checkpoint&quot;)  \r\n.outputMode(&quot;append&quot;)  \r\n.start()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434465[]' id='answer-id-1681162' class='answer   answerof-434465 ' value='1681162'   \/><label for='answer-id-1681162' id='answer-label-1681162' class=' answer'><span>query = streaming_df.writeStream  \r\n.format(&quot;console&quot;)  \r\n.outputMode(&quot;append&quot;)  \r\n.option(&quot;checkpointLocation&quot;, &quot;\/path\/to\/checkpoint&quot;)  \r\n.start()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434465[]' id='answer-id-1681163' class='answer   answerof-434465 ' value='1681163'   \/><label for='answer-id-1681163' id='answer-label-1681163' class=' answer'><span>query = streaming_df.writeStream  \r\n.format(&quot;console&quot;)  \r\n.outputMode(&quot;complete&quot;)  \r\n.start()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434465[]' id='answer-id-1681164' class='answer   answerof-434465 ' value='1681164'   \/><label for='answer-id-1681164' id='answer-label-1681164' class=' answer'><span>query = streaming_df.writeStream  \r\n.format(&quot;console&quot;)  \r\n.outputMode(&quot;append&quot;)  \r\n.start()<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-47' style=';'><div id='questionWrap-47'  class='   watupro-question-id-434466'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>47. <\/span>Given: <br \/>\r<br>python <br \/>\r<br>CopyEdit <br \/>\r<br>spark.sparkContext.setLogLevel(&quot;&lt;LOG_LEVEL&gt;&quot;) <br \/>\r<br>Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?<\/div><input type='hidden' name='question_id[]' id='qID_47' value='434466' \/><input type='hidden' id='answerType434466' 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-434466[]' id='answer-id-1681165' class='answer   answerof-434466 ' value='1681165'   \/><label for='answer-id-1681165' id='answer-label-1681165' class=' answer'><span>ALL, DEBUG, FAIL, INFO<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434466[]' id='answer-id-1681166' class='answer   answerof-434466 ' value='1681166'   \/><label for='answer-id-1681166' id='answer-label-1681166' class=' answer'><span>ERROR, WARN, TRACE, OFF<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434466[]' id='answer-id-1681167' class='answer   answerof-434466 ' value='1681167'   \/><label for='answer-id-1681167' id='answer-label-1681167' class=' answer'><span>WARN, NONE, ERROR, FATAL<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434466[]' id='answer-id-1681168' class='answer   answerof-434466 ' value='1681168'   \/><label for='answer-id-1681168' id='answer-label-1681168' class=' answer'><span>FATAL, NONE, INFO, DEBUG<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-48' style=';'><div id='questionWrap-48'  class='   watupro-question-id-434467'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>48. <\/span>A developer wants to test Spark Connect with an existing Spark application. <br \/>\r<br>What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)<\/div><input type='hidden' name='question_id[]' id='qID_48' value='434467' \/><input type='hidden' id='answerType434467' 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-434467[]' id='answer-id-1681169' class='answer   answerof-434467 ' value='1681169'   \/><label for='answer-id-1681169' id='answer-label-1681169' class=' answer'><span>Execute their pyspark shell with the option --remote &quot;https:\/\/localhost&quot;<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434467[]' id='answer-id-1681170' class='answer   answerof-434467 ' value='1681170'   \/><label for='answer-id-1681170' id='answer-label-1681170' class=' answer'><span>Execute their pyspark shell with the option --remote &quot;sc:\/\/localhost&quot;<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434467[]' id='answer-id-1681171' class='answer   answerof-434467 ' value='1681171'   \/><label for='answer-id-1681171' id='answer-label-1681171' class=' answer'><span>Set the environment variable SPARK_REMOTE=&quot;sc:\/\/localhost&quot; before starting the pyspark shell<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434467[]' id='answer-id-1681172' class='answer   answerof-434467 ' value='1681172'   \/><label for='answer-id-1681172' id='answer-label-1681172' class=' answer'><span>Add .remote(&quot;sc:\/\/localhost&quot;) to their SparkSession.builder calls in their Spark code<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-434467[]' id='answer-id-1681173' class='answer   answerof-434467 ' value='1681173'   \/><label for='answer-id-1681173' id='answer-label-1681173' class=' answer'><span>Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-49' style=';'><div id='questionWrap-49'  class='   watupro-question-id-434468'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>49. <\/span>A Data Analyst needs to retrieve employees with 5 or more years of tenure. <br \/>\r<br>Which code snippet filters and shows the list?<\/div><input type='hidden' name='question_id[]' id='qID_49' value='434468' \/><input type='hidden' id='answerType434468' 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-434468[]' id='answer-id-1681174' class='answer   answerof-434468 ' value='1681174'   \/><label for='answer-id-1681174' id='answer-label-1681174' class=' answer'><span>employees_df.filter(employees_df.tenure &gt;= 5).show()<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434468[]' id='answer-id-1681175' class='answer   answerof-434468 ' value='1681175'   \/><label for='answer-id-1681175' id='answer-label-1681175' class=' answer'><span>employees_df.where(employees_df.tenure &gt;= 5)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434468[]' id='answer-id-1681176' class='answer   answerof-434468 ' value='1681176'   \/><label for='answer-id-1681176' id='answer-label-1681176' class=' answer'><span>filter(employees_df.tenure &gt;= 5)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434468[]' id='answer-id-1681177' class='answer   answerof-434468 ' value='1681177'   \/><label for='answer-id-1681177' id='answer-label-1681177' class=' answer'><span>employees_df.filter(employees_df.tenure &gt;= 5).collect()<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-50' style=';'><div id='questionWrap-50'  class='   watupro-question-id-434469'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>50. <\/span>A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled. <br \/>\r<br>Which operation is AQE implementing to improve performance?<\/div><input type='hidden' name='question_id[]' id='qID_50' value='434469' \/><input type='hidden' id='answerType434469' 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-434469[]' id='answer-id-1681178' class='answer   answerof-434469 ' value='1681178'   \/><label for='answer-id-1681178' id='answer-label-1681178' class=' answer'><span>Dynamically switching join strategies<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434469[]' id='answer-id-1681179' class='answer   answerof-434469 ' value='1681179'   \/><label for='answer-id-1681179' id='answer-label-1681179' class=' answer'><span>Collecting persistent table statistics and storing them in the metastore for future use<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434469[]' id='answer-id-1681180' class='answer   answerof-434469 ' value='1681180'   \/><label for='answer-id-1681180' id='answer-label-1681180' class=' answer'><span>Improving the performance of single-stage Spark jobs<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-434469[]' id='answer-id-1681181' class='answer   answerof-434469 ' value='1681181'   \/><label for='answer-id-1681181' id='answer-label-1681181' class=' answer'><span>Optimizing the layout of Delta files on disk<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-51'>\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=\"watuPROButtons11032\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; 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   \t \n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>Focus on the latest information, the Databricks Certified Associate Developer for Apache Spark has been upgraded to version 3.5. Now, you must register for the Databricks Certified Associate Developer for Apache Spark 3.5 exam and achieve success smoothly. 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