{"id":115128,"date":"2025-11-24T07:17:30","date_gmt":"2025-11-24T07:17:30","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=115128"},"modified":"2025-11-26T01:31:10","modified_gmt":"2025-11-26T01:31:10","slug":"real-ges-c01-dumps-v8-02-for-the-snowpro-specialty-gen-ai-certification-exam-preparation-check-ges-c01-free-dumps-part-1-q1-q40-first","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/real-ges-c01-dumps-v8-02-for-the-snowpro-specialty-gen-ai-certification-exam-preparation-check-ges-c01-free-dumps-part-1-q1-q40-first.html","title":{"rendered":"Real GES-C01 Dumps (V8.02) for the SnowPro Specialty: Gen AI Certification Exam Preparation: Check GES-C01 Free Dumps (Part 1, Q1-Q40) First"},"content":{"rendered":"<p>The SnowPro Specialty: Gen AI (GES-C01) is available to validate specialized knowledge, skills, and best practices for leveraging Gen AI methodologies in Snowflake, including key concepts, features, and programming constructs. During your GES-C01 exam preparation, you can choose real GES-C01 dumps (V8.02) from DumpsBase. We have the GES-C01 exam questions with verified answers that help you understand the real exam requirements. Using these GES-C01 practice test questions allows you to simulate real exam conditions. This is vital because the dumps rely heavily on familiarity with the exam pattern and objectives. Our GES-C01 dumps are designed to replicate a real exam experience, helping you familiarize yourself with question types, difficulty levels, and timing. Furthermore, we have free dumps online to help you check the quality first.<\/p>\n<h2>You can check our <span style=\"background-color: #99ccff;\"><em>GES-C01 free dumps (Part 1, Q1-Q40) of V8.02 below<\/em><\/span> to verify the quality:<\/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=\"submittingExam11226\" 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-11226\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11226\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-441479'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A data engineer is constructing a Retrieval Augmented Generation (RAG) pipeline in Snowflake to allow users to query a large corpus of unstructured customer support transcripts using natural language. The goal is to retrieve relevant transcript snippets and then use a Large Language Model (LLM) to generate an answer. <br \/>\r<br>Which sequence of steps and Snowflake components would effectively implement this RAG pipeline? <br \/>\r<br><br><img decoding=\"async\" width=623 height=146 id=\"Picture 305\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image258.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_1' value='441479' \/><input type='hidden' id='answerType441479' 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-441479[]' id='answer-id-1708076' class='answer   answerof-441479 ' value='1708076'   \/><label for='answer-id-1708076' id='answer-label-1708076' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441479[]' id='answer-id-1708077' class='answer   answerof-441479 ' value='1708077'   \/><label for='answer-id-1708077' id='answer-label-1708077' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441479[]' id='answer-id-1708078' class='answer   answerof-441479 ' value='1708078'   \/><label for='answer-id-1708078' id='answer-label-1708078' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441479[]' id='answer-id-1708079' class='answer   answerof-441479 ' value='1708079'   \/><label for='answer-id-1708079' id='answer-label-1708079' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441479[]' id='answer-id-1708080' class='answer   answerof-441479 ' value='1708080'   \/><label for='answer-id-1708080' id='answer-label-1708080' class=' answer'><span>Option E<\/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-441480'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='441480' \/><input type='hidden' id='answerType441480' 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-441480[]' id='answer-id-1708081' class='answer   answerof-441480 ' value='1708081'   \/><label for='answer-id-1708081' id='answer-label-1708081' class=' answer'><span>Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441480[]' id='answer-id-1708082' class='answer   answerof-441480 ' value='1708082'   \/><label for='answer-id-1708082' id='answer-label-1708082' class=' answer'><span>To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441480[]' id='answer-id-1708083' class='answer   answerof-441480 ' value='1708083'   \/><label for='answer-id-1708083' id='answer-label-1708083' class=' answer'><span>When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441480[]' id='answer-id-1708084' class='answer   answerof-441480 ' value='1708084'   \/><label for='answer-id-1708084' id='answer-label-1708084' class=' answer'><span>Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441480[]' id='answer-id-1708085' class='answer   answerof-441480 ' value='1708085'   \/><label for='answer-id-1708085' id='answer-label-1708085' class=' answer'><span>The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.<\/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-441481'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A data scientist is preparing to log a custom PyCaret classification model into the Snowflake Model Registry. The goal is to deploy this model on Snowpark Container Services (SPCS) for scalable inference. The PyCaret model relies on the 'pycaret' and 'scipy' Python libraries, and the data scientist has local 'sample data.csv' for inferring the model's signature. <br \/>\r<br>Which statements are crucial for successfully logging this custom model for eventual SPCS deployment? <br \/>\r<br><br><img decoding=\"async\" width=625 height=121 id=\"Picture 657\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image084-8.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_3' value='441481' \/><input type='hidden' id='answerType441481' 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-441481[]' id='answer-id-1708086' class='answer   answerof-441481 ' value='1708086'   \/><label for='answer-id-1708086' id='answer-label-1708086' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441481[]' id='answer-id-1708087' class='answer   answerof-441481 ' value='1708087'   \/><label for='answer-id-1708087' id='answer-label-1708087' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441481[]' id='answer-id-1708088' class='answer   answerof-441481 ' value='1708088'   \/><label for='answer-id-1708088' id='answer-label-1708088' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441481[]' id='answer-id-1708089' class='answer   answerof-441481 ' value='1708089'   \/><label for='answer-id-1708089' id='answer-label-1708089' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441481[]' id='answer-id-1708090' class='answer   answerof-441481 ' value='1708090'   \/><label for='answer-id-1708090' id='answer-label-1708090' class=' answer'><span>Option E<\/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-441484'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A data scientist is designing a real-time similarity search feature in Snowflake using product embeddings. They plan to use VECTOR_L2_DISTANCE to find similar products. <br \/>\r<br>Which statement correctly identifies a cost or data type characteristic relevant to this implementation?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='441484' \/><input type='hidden' id='answerType441484' 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-441484[]' id='answer-id-1708096' class='answer   answerof-441484 ' value='1708096'   \/><label for='answer-id-1708096' id='answer-label-1708096' class=' answer'><span>The VECTOR_L2_DISTANCE function incurs compute costs based on the square root of the number of output tokens generated.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441484[]' id='answer-id-1708097' class='answer   answerof-441484 ' value='1708097'   \/><label for='answer-id-1708097' id='answer-label-1708097' class=' answer'><span>Storing product embeddings generated by EMBED_TEXT_768 in a VECTOR(INT, 768) column is a valid and efficient data type choice for the embeddings.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441484[]' id='answer-id-1708098' class='answer   answerof-441484 ' value='1708098'   \/><label for='answer-id-1708098' id='answer-label-1708098' class=' answer'><span>Both the EMBED_TEXT_768 function and VECTOR_L2_DISTANCE incur token-based compute costs, but EMBED_TEXT_768 also includes a fixed per-call fee.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441484[]' id='answer-id-1708099' class='answer   answerof-441484 ' value='1708099'   \/><label for='answer-id-1708099' id='answer-label-1708099' class=' answer'><span>The VECTOR_L2_DISTANCE function itself does not incur token-based compute costs, distinguishing it from embedding generation functions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441484[]' id='answer-id-1708100' class='answer   answerof-441484 ' value='1708100'   \/><label for='answer-id-1708100' id='answer-label-1708100' class=' answer'><span>The maximum dimension supported for a VECTOR data type in Snowflake is 768, aligning with common embedding model outputs.<\/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-441485'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. <br \/>\r<br>Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline? <br \/>\r<br><br><img decoding=\"async\" width=623 height=148 id=\"Picture 410\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image010-16.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_5' value='441485' \/><input type='hidden' id='answerType441485' 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-441485[]' id='answer-id-1708101' class='answer   answerof-441485 ' value='1708101'   \/><label for='answer-id-1708101' id='answer-label-1708101' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441485[]' id='answer-id-1708102' class='answer   answerof-441485 ' value='1708102'   \/><label for='answer-id-1708102' id='answer-label-1708102' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441485[]' id='answer-id-1708103' class='answer   answerof-441485 ' value='1708103'   \/><label for='answer-id-1708103' id='answer-label-1708103' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441485[]' id='answer-id-1708104' class='answer   answerof-441485 ' value='1708104'   \/><label for='answer-id-1708104' id='answer-label-1708104' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441485[]' id='answer-id-1708105' class='answer   answerof-441485 ' value='1708105'   \/><label for='answer-id-1708105' id='answer-label-1708105' class=' answer'><span>Option E<\/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-441488'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A Snowflake Gen AI Specialist is defining a semantic model for Cortex Analyst to improve text-to-SQL accuracy. They are adding entries to the verified _ queries section of their YAML file. Consider the following semantic model snippet and a proposed verified_query entry. <br \/>\r<br>Which of the following statements correctly identifies an issue or a best practice not followed in the sql field of the proposed verified_query entry, based on Cortex Analyst VQR guidelines? Semantic Model Snippet: <br \/>\r<br><br><img decoding=\"async\" width=316 height=400 id=\"Picture 275\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image246.jpg\"><br><br \/>\r<br>Proposed verified _ query entry: <br \/>\r<br><br><img decoding=\"async\" width=623 height=51 id=\"Picture 278\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image247.jpg\"><br><br \/>\r<br><br><img decoding=\"async\" width=624 height=100 id=\"Picture 279\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image248.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_6' value='441488' \/><input type='hidden' id='answerType441488' 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-441488[]' id='answer-id-1708108' class='answer   answerof-441488 ' value='1708108'   \/><label for='answer-id-1708108' id='answer-label-1708108' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441488[]' id='answer-id-1708109' class='answer   answerof-441488 ' value='1708109'   \/><label for='answer-id-1708109' id='answer-label-1708109' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441488[]' id='answer-id-1708110' class='answer   answerof-441488 ' value='1708110'   \/><label for='answer-id-1708110' id='answer-label-1708110' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441488[]' id='answer-id-1708111' class='answer   answerof-441488 ' value='1708111'   \/><label for='answer-id-1708111' id='answer-label-1708111' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441488[]' id='answer-id-1708112' class='answer   answerof-441488 ' value='1708112'   \/><label for='answer-id-1708112' id='answer-label-1708112' class=' answer'><span>Option E<\/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-441494'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A Gen AI Specialist is responsible for maintaining a Cortex Analyst-powered application. They have defined a semantic model that includes a Verified Query Repository (VQR) to guide user interactions. The application front-end uses the Suggested Questions feature to help users get started. The specialist wants to ensure that a specific set of critical, verified business questions are always displayed to users, regardless of their prior input or the semantic similarity to their current query. <br \/>\r<br>Which of the following configuration steps in the semantic model YAML will achieve this requirement? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=624 height=13 id=\"Picture 286\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image251.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=624 height=18 id=\"Picture 289\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image252.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=623 height=15 id=\"Picture 292\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image253.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=624 height=12 id=\"Picture 295\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image254.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=624 height=14 id=\"Picture 298\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image255.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_7' value='441494' \/><input type='hidden' id='answerType441494' 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-441494[]' id='answer-id-1708126' class='answer   answerof-441494 ' value='1708126'   \/><label for='answer-id-1708126' id='answer-label-1708126' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441494[]' id='answer-id-1708128' class='answer   answerof-441494 ' value='1708128'   \/><label for='answer-id-1708128' id='answer-label-1708128' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441494[]' id='answer-id-1708130' class='answer   answerof-441494 ' value='1708130'   \/><label for='answer-id-1708130' id='answer-label-1708130' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441494[]' id='answer-id-1708131' class='answer   answerof-441494 ' value='1708131'   \/><label for='answer-id-1708131' id='answer-label-1708131' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441494[]' id='answer-id-1708133' class='answer   answerof-441494 ' value='1708133'   \/><label for='answer-id-1708133' id='answer-label-1708133' class=' answer'><span>Option E<\/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-441498'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process? <br \/>\r<br><br><img decoding=\"async\" width=623 height=125 id=\"Picture 431\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image017-14.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_8' value='441498' \/><input type='hidden' id='answerType441498' 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-441498[]' id='answer-id-1708143' class='answer   answerof-441498 ' value='1708143'   \/><label for='answer-id-1708143' id='answer-label-1708143' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441498[]' id='answer-id-1708144' class='answer   answerof-441498 ' value='1708144'   \/><label for='answer-id-1708144' id='answer-label-1708144' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441498[]' id='answer-id-1708146' class='answer   answerof-441498 ' value='1708146'   \/><label for='answer-id-1708146' id='answer-label-1708146' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441498[]' id='answer-id-1708148' class='answer   answerof-441498 ' value='1708148'   \/><label for='answer-id-1708148' id='answer-label-1708148' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441498[]' id='answer-id-1708150' class='answer   answerof-441498 ' value='1708150'   \/><label for='answer-id-1708150' id='answer-label-1708150' class=' answer'><span>Option E<\/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-441501'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A data scientist wants to fine-tune a mistral -7b model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns <br \/>\r<br>PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). <br \/>\r<br>Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex? (Select all that apply)<\/div><input type='hidden' name='question_id[]' id='qID_9' value='441501' \/><input type='hidden' id='answerType441501' 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-441501[]' id='answer-id-1708155' class='answer   answerof-441501 ' value='1708155'   \/><label for='answer-id-1708155' id='answer-label-1708155' class=' answer'><span>The FINETUNE function requires that the training data explicitly includes a system role message to define the model's persona for optimal output during fine-tuning.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441501[]' id='answer-id-1708156' class='answer   answerof-441501 ' value='1708156'   \/><label for='answer-id-1708156' id='answer-label-1708156' class=' answer'><span>The SQL query for the training data must select columns aliased as prompt and completion, such as: \r\n<br><img decoding=\"async\" width=624 height=15 id=\"Picture 1\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image145-2.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441501[]' id='answer-id-1708157' class='answer   answerof-441501 ' value='1708157'   \/><label for='answer-id-1708157' id='answer-label-1708157' class=' answer'><span>The fine-tuning job must be created using a CREATE SNOWFLAK<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441501[]' id='answer-id-1708158' class='answer   answerof-441501 ' value='1708158'   \/><label for='answer-id-1708158' id='answer-label-1708158' class=' answer'><span>M<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441501[]' id='answer-id-1708159' class='answer   answerof-441501 ' value='1708159'   \/><label for='answer-id-1708159' id='answer-label-1708159' class=' answer'><span>FINETUNE command, similar to how ANOMALY_DETECTION models are created, to register the model object.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441501[]' id='answer-id-1708160' class='answer   answerof-441501 ' value='1708160'   \/><label for='answer-id-1708160' id='answer-label-1708160' class=' answer'><span>O To generate highly structured completion data for fine-tuning, the AI_COMPLETE function with the response_format argument can be used in a prior step to ensure JSON adherence.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441501[]' id='answer-id-1708161' class='answer   answerof-441501 ' value='1708161'   \/><label for='answer-id-1708161' id='answer-label-1708161' class=' answer'><span>Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.<\/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-441504'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A Gen AI Specialist is setting up their Snowflake environment to deploy a high-performance open-source LLM for real-time inference using Snowpark Container Services (SPCS). They need to create a compute pool that can leverage NVIDIAAIOG GPUs to optimize model performance. <br \/>\r<br>Which of the following SQL statements correctly creates a compute pool capable of supporting an intensive GPU usage scenario, such as serving LLMs, while adhering to common configuration best practices for a new, small-scale deployment in Snowpark Container Services? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=278 height=113 id=\"Picture 487\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image031-9.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=280 height=116 id=\"Picture 490\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image032-8.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=299 height=111 id=\"Picture 493\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image033-8.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=279 height=118 id=\"Picture 496\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image034-9.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=281 height=113 id=\"Picture 499\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image035-9.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_10' value='441504' \/><input type='hidden' id='answerType441504' 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-441504[]' id='answer-id-1708169' class='answer   answerof-441504 ' value='1708169'   \/><label for='answer-id-1708169' id='answer-label-1708169' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441504[]' id='answer-id-1708170' class='answer   answerof-441504 ' value='1708170'   \/><label for='answer-id-1708170' id='answer-label-1708170' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441504[]' id='answer-id-1708171' class='answer   answerof-441504 ' value='1708171'   \/><label for='answer-id-1708171' id='answer-label-1708171' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441504[]' id='answer-id-1708172' class='answer   answerof-441504 ' value='1708172'   \/><label for='answer-id-1708172' id='answer-label-1708172' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441504[]' id='answer-id-1708173' class='answer   answerof-441504 ' value='1708173'   \/><label for='answer-id-1708173' id='answer-label-1708173' class=' answer'><span>Option E<\/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-441508'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>An administrator has configured the 'CORTEX MODELS ALLOWLIST' parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE.\"CORTEX- MODEL-ROLE-LLAMA3.1-70B\"', attempts to execute several 'AI_COMPLETE queries.<br \/>\r\n<br \/>\r\nWhich of the following queries will successfully execute?<br \/>\r\n<br \/>\r\nA)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"Picture 678\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image091-7.jpg\" width=\"397\" height=\"22\" \/>B)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"Picture 681\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image092-7.jpg\" width=\"549\" height=\"20\" \/>C)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"Picture 684\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image093-7.jpg\" width=\"385\" height=\"20\" \/>D)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"Picture 687\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image094-7.jpg\" width=\"416\" height=\"19\" \/>E)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"Picture 690\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image095-6.jpg\" width=\"548\" height=\"20\" \/><\/div><input type='hidden' name='question_id[]' id='qID_11' value='441508' \/><input type='hidden' id='answerType441508' 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-441508[]' id='answer-id-1708181' class='answer   answerof-441508 ' value='1708181'   \/><label for='answer-id-1708181' id='answer-label-1708181' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441508[]' id='answer-id-1709290' class='answer   answerof-441508 ' value='1709290'   \/><label for='answer-id-1709290' id='answer-label-1709290' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441508[]' id='answer-id-1709291' class='answer   answerof-441508 ' value='1709291'   \/><label for='answer-id-1709291' id='answer-label-1709291' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441508[]' id='answer-id-1709292' class='answer   answerof-441508 ' value='1709292'   \/><label for='answer-id-1709292' id='answer-label-1709292' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441508[]' id='answer-id-1709293' class='answer   answerof-441508 ' value='1709293'   \/><label for='answer-id-1709293' id='answer-label-1709293' class=' answer'><span>Option E<\/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-441509'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A company is building an enterprise search solution in Snowflake, where user queries are converted into embeddings and then used to find relevant documents from a large corpus. The search logic heavily relies on VECTOR_COSINE_SIMILARITY <br \/>\r<br>Which of the following design choices or operational considerations are critical for a robust and efficient implementation using Snowflake's vector capabilities? (Select all that apply)<\/div><input type='hidden' name='question_id[]' id='qID_12' value='441509' \/><input type='hidden' id='answerType441509' 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-441509[]' id='answer-id-1708182' class='answer   answerof-441509 ' value='1708182'   \/><label for='answer-id-1708182' id='answer-label-1708182' class=' answer'><span>To keep document embeddings updated efficiently, a VECTOR column should be designated as a clustering key on the document table, optimizing similarity search queries.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441509[]' id='answer-id-1708183' class='answer   answerof-441509 ' value='1708183'   \/><label for='answer-id-1708183' id='answer-label-1708183' class=' answer'><span>Storing document embeddings in a VARIANT column offers maximum flexibility, allowing VECTOR_COSINE_SIMILARITY to operate after an explicit cast to a VECTOR type.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441509[]' id='answer-id-1708184' class='answer   answerof-441509 ' value='1708184'   \/><label for='answer-id-1708184' id='answer-label-1708184' class=' answer'><span>For improved retrieval quality in RAG scenarios, it is recommended to split text into smaller chunks, ideally no more than 512 tokens, before generating embeddings for subsequent VECTOR_COSINE_SIMILARITY operations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441509[]' id='answer-id-1708185' class='answer   answerof-441509 ' value='1708185'   \/><label for='answer-id-1708185' id='answer-label-1708185' class=' answer'><span>Bind variables can be used to pass query vector literals securely and efficiently to VECTOR_COSINE_SIMILARITY in SQL queries, improving performance by reusing query plans.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441509[]' id='answer-id-1708186' class='answer   answerof-441509 ' value='1708186'   \/><label for='answer-id-1708186' id='answer-label-1708186' class=' answer'><span>When deploying custom embedding models or complex search logic, Snowpark Container Services can host GPU-accelerated environments, while VECTOR_COSINE_SIMILARITY can still be called on native VECTOR columns within Snowflake's SQL layer.<\/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-441513'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>An AI developer is testing a new RAG application in Snowflake. <br \/>\r<br><br><img decoding=\"async\" width=624 height=224 id=\"Picture 193\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image213.jpg\"><br><br \/>\r<br>The application uses in this scenario? <br \/>\r<br><br><img decoding=\"async\" width=623 height=340 id=\"Picture 196\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image214.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_13' value='441513' \/><input type='hidden' id='answerType441513' 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-441513[]' id='answer-id-1708191' class='answer   answerof-441513 ' value='1708191'   \/><label for='answer-id-1708191' id='answer-label-1708191' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441513[]' id='answer-id-1708192' class='answer   answerof-441513 ' value='1708192'   \/><label for='answer-id-1708192' id='answer-label-1708192' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441513[]' id='answer-id-1708193' class='answer   answerof-441513 ' value='1708193'   \/><label for='answer-id-1708193' id='answer-label-1708193' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441513[]' id='answer-id-1708194' class='answer   answerof-441513 ' value='1708194'   \/><label for='answer-id-1708194' id='answer-label-1708194' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441513[]' id='answer-id-1708195' class='answer   answerof-441513 ' value='1708195'   \/><label for='answer-id-1708195' id='answer-label-1708195' class=' answer'><span>Option E<\/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-441516'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role \u2018DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet\u2019 model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=624 height=74 id=\"Picture 735\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image110-6.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=624 height=59 id=\"Picture 738\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image111-7.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=624 height=62 id=\"Picture 741\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image112-7.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=625 height=75 id=\"Picture 744\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image113-5.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=556 height=72 id=\"Picture 747\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image114-5.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_14' value='441516' \/><input type='hidden' id='answerType441516' 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-441516[]' id='answer-id-1708198' class='answer   answerof-441516 ' value='1708198'   \/><label for='answer-id-1708198' id='answer-label-1708198' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441516[]' id='answer-id-1708201' class='answer   answerof-441516 ' value='1708201'   \/><label for='answer-id-1708201' id='answer-label-1708201' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441516[]' id='answer-id-1708202' class='answer   answerof-441516 ' value='1708202'   \/><label for='answer-id-1708202' id='answer-label-1708202' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441516[]' id='answer-id-1708204' class='answer   answerof-441516 ' value='1708204'   \/><label for='answer-id-1708204' id='answer-label-1708204' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441516[]' id='answer-id-1708206' class='answer   answerof-441516 ' value='1708206'   \/><label for='answer-id-1708206' id='answer-label-1708206' class=' answer'><span>Option E<\/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-441521'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A data scientist has fine-tuned a Hugging Face sentence transformer model for semantic search and intends to deploy it to Snowpark Container Services (SPCS) via the Snowflake Model Registry. The model requires GPU acceleration and specific Python packages ('sentence-transformerS, 'torch', 'transformers'). A GPU compute pool named 'my_gpu_pool' is available. <br \/>\r<br>Which of the following code snippets correctly logs the model and deploys it as a service to SPCS, ensuring it utilizes the GPU compute pool and has the necessary Python dependencies for the Hugging Face model and PyTorch? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=625 height=237 id=\"Picture 530\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image045-6.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=624 height=220 id=\"Picture 533\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image046-6.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=624 height=221 id=\"Picture 542\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image047-7.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=624 height=222 id=\"Picture 545\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image048-6.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=625 height=237 id=\"Picture 548\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image049-6.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_15' value='441521' \/><input type='hidden' id='answerType441521' 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-441521[]' id='answer-id-1708211' class='answer   answerof-441521 ' value='1708211'   \/><label for='answer-id-1708211' id='answer-label-1708211' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441521[]' id='answer-id-1708212' class='answer   answerof-441521 ' value='1708212'   \/><label for='answer-id-1708212' id='answer-label-1708212' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441521[]' id='answer-id-1708213' class='answer   answerof-441521 ' value='1708213'   \/><label for='answer-id-1708213' id='answer-label-1708213' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441521[]' id='answer-id-1708214' class='answer   answerof-441521 ' value='1708214'   \/><label for='answer-id-1708214' id='answer-label-1708214' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441521[]' id='answer-id-1708215' class='answer   answerof-441521 ' value='1708215'   \/><label for='answer-id-1708215' id='answer-label-1708215' class=' answer'><span>Option E<\/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-441522'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A data engineering team aims to automatically classify incoming customer support requests into predefined categories ('Technical Issue', 'Billing Inquiry', 'General Question') as part of their Snowflake data ingestion pipeline. The goal is to achieve high classification accuracy while managing LLM inference costs efficiently. <br \/>\r<br>Which of the following strategies, when applied within a Snowflake data pipeline using Streams and Tasks, would best contribute to meeting these objectives? <br \/>\r<br><br><img decoding=\"async\" width=624 height=147 id=\"Picture 438\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image020-13.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_16' value='441522' \/><input type='hidden' id='answerType441522' 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-441522[]' id='answer-id-1708217' class='answer   answerof-441522 ' value='1708217'   \/><label for='answer-id-1708217' id='answer-label-1708217' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441522[]' id='answer-id-1708218' class='answer   answerof-441522 ' value='1708218'   \/><label for='answer-id-1708218' id='answer-label-1708218' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441522[]' id='answer-id-1708220' class='answer   answerof-441522 ' value='1708220'   \/><label for='answer-id-1708220' id='answer-label-1708220' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441522[]' id='answer-id-1708222' class='answer   answerof-441522 ' value='1708222'   \/><label for='answer-id-1708222' id='answer-label-1708222' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441522[]' id='answer-id-1708224' class='answer   answerof-441522 ' value='1708224'   \/><label for='answer-id-1708224' id='answer-label-1708224' class=' answer'><span>Option E<\/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-441530'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. <br \/>\r<br>Which of the following are \u2018\u2018essential\u2019\u2019 components or configurations that must be included in the 'spec.yaml\u2019 file for a long- running service that uses this image, custom environment variables, and requires external access? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=624 height=80 id=\"Picture 581\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image060-8.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=156 height=111 id=\"Picture 584\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image061-7.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=239 height=41 id=\"Picture 587\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image062-8.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=264 height=87 id=\"Picture 590\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image063-8.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=463 height=115 id=\"Picture 593\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image064-6.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_17' value='441530' \/><input type='hidden' id='answerType441530' 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-441530[]' id='answer-id-1708241' class='answer   answerof-441530 ' value='1708241'   \/><label for='answer-id-1708241' id='answer-label-1708241' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441530[]' id='answer-id-1708242' class='answer   answerof-441530 ' value='1708242'   \/><label for='answer-id-1708242' id='answer-label-1708242' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441530[]' id='answer-id-1708243' class='answer   answerof-441530 ' value='1708243'   \/><label for='answer-id-1708243' id='answer-label-1708243' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441530[]' id='answer-id-1708244' class='answer   answerof-441530 ' value='1708244'   \/><label for='answer-id-1708244' id='answer-label-1708244' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441530[]' id='answer-id-1708245' class='answer   answerof-441530 ' value='1708245'   \/><label for='answer-id-1708245' id='answer-label-1708245' class=' answer'><span>Option E<\/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-441531'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A data team is designing a new Cortex Analyst application and wants to ensure optimal performance, accuracy, and user experience for text-to-SQL conversions. They are particularly interested in how custom instructions interact with other semantic model features and LLM functionalities. <br \/>\r<br>Which of the following statements about using in Cortex Analyst are accurate?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='441531' \/><input type='hidden' id='answerType441531' 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-441531[]' id='answer-id-1708246' class='answer   answerof-441531 ' value='1708246'   \/><label for='answer-id-1708246' id='answer-label-1708246' class=' answer'><span>The \u2018custom_instructions\u2019 in a semantic model directly influence the underlying Large Language Model (LLM) to generate SQL queries that align with specified business context or formatting preferences.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441531[]' id='answer-id-1708247' class='answer   answerof-441531 ' value='1708247'   \/><label for='answer-id-1708247' id='answer-label-1708247' class=' answer'><span>When both \u2018custom_instructionS and a highly relevant \u2018verified_query\u2019 exist for a user's question, Cortex Analyst will always prioritize the directives from the \u2018custom_instructions\u2019 over the SQL provided in the 'verified_querv\u2019.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441531[]' id='answer-id-1708248' class='answer   answerof-441531 ' value='1708248'   \/><label for='answer-id-1708248' id='answer-label-1708248' class=' answer'><span>Using detailed \u2018custom_instructions\u2019 can help mitigate issues where the LLM might struggle with domain-specific terminology or complex business logic not explicitly defined in column descriptions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441531[]' id='answer-id-1708249' class='answer   answerof-441531 ' value='1708249'   \/><label for='answer-id-1708249' id='answer-label-1708249' class=' answer'><span>The presence of \u2018custom_instructions\u2019 in a semantic model can potentially increase the token count for Cortex Analyst requests, as the instructions are passed as additional context to the LL<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441531[]' id='answer-id-1708250' class='answer   answerof-441531 ' value='1708250'   \/><label for='answer-id-1708250' id='answer-label-1708250' class=' answer'><span>Custom instructions are primarily used to define new logical tables or dimensions within the semantic model, effectively extending the data model at runtime.<\/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-441532'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A Gen AI specialist is designing an intelligent document processing workflow using Snowflake Cortex AI PARSE DOCUMENT to handle various types of documents, including scanned research papers, financial 10-K filings with tables, and multilingual presentations. <br \/>\r<br>Which of the following statements accurately describe the capabilities and operational modes of Snowflake's AI_PARSE_DOCUMENT function when processing these diverse documents? <br \/>\r<br><br><img decoding=\"async\" width=624 height=133 id=\"Picture 256\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image237.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_19' value='441532' \/><input type='hidden' id='answerType441532' 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-441532[]' id='answer-id-1708251' class='answer   answerof-441532 ' value='1708251'   \/><label for='answer-id-1708251' id='answer-label-1708251' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441532[]' id='answer-id-1708252' class='answer   answerof-441532 ' value='1708252'   \/><label for='answer-id-1708252' id='answer-label-1708252' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441532[]' id='answer-id-1708253' class='answer   answerof-441532 ' value='1708253'   \/><label for='answer-id-1708253' id='answer-label-1708253' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441532[]' id='answer-id-1708254' class='answer   answerof-441532 ' value='1708254'   \/><label for='answer-id-1708254' id='answer-label-1708254' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441532[]' id='answer-id-1708255' class='answer   answerof-441532 ' value='1708255'   \/><label for='answer-id-1708255' id='answer-label-1708255' class=' answer'><span>Option E<\/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-441533'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A development team plans to utilize Snowpark Container Services (SPCS) for deploying a variety of AI\/ML workloads, including custom LLMs and GPU-accelerated model training jobs. They are in the process of creating a compute pool and need to select the appropriate instance families and configurations. <br \/>\r<br>Which of the following statements about 'CREATE COMPUTE POOL' in SPCS are accurate?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='441533' \/><input type='hidden' id='answerType441533' 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-441533[]' id='answer-id-1708256' class='answer   answerof-441533 ' value='1708256'   \/><label for='answer-id-1708256' id='answer-label-1708256' class=' answer'><span>To support GPU-accelerated LLM inference and training, the \u2018INSTANCE_FAMILY\u2019 must be selected from a type starting with 'GPU' (e.g., <br><img decoding=\"async\" width=169 height=130 id=\"Picture 617\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image070-7.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441533[]' id='answer-id-1708257' class='answer   answerof-441533 ' value='1708257'   \/><label for='answer-id-1708257' id='answer-label-1708257' class=' answer'><span>The \u2018MIN NODES' and 'MAX NODES parameters define the scaling range for the compute pool, and Snowflake automatically scales the pool within this range based on workload demand.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441533[]' id='answer-id-1708258' class='answer   answerof-441533 ' value='1708258'   \/><label for='answer-id-1708258' id='answer-label-1708258' class=' answer'><span>Setting \u2018AUTO RESUME = TRUE ensures that the compute pool automatically starts when a service or job is submitted to it, rather than requiring manual resumption.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441533[]' id='answer-id-1708259' class='answer   answerof-441533 ' value='1708259'   \/><label for='answer-id-1708259' id='answer-label-1708259' class=' answer'><span>For cost optimization, 'AUTO SUSPEND SECS = 0' should be used to prevent automatic suspension of the compute pool, as suspension and resumption incur minimum billing durations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441533[]' id='answer-id-1708260' class='answer   answerof-441533 ' value='1708260'   \/><label for='answer-id-1708260' id='answer-label-1708260' class=' answer'><span>Snowpark-optimized warehouses are the recommended compute pool type for all large-scale ML training workloads within SPCS due to their enhanced memory limits and CPU architectures.<\/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-441534'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A financial institution uses Snowflake Cortex LLM functions to process customer feedback. They initially used SNOWF LAKE.CORTEX.SENTIMENT for general sentiment analysis. Now, they need to extract specific sentiment categories (e.g., 'service_quality', 'product_pricing') and the sentiment for each, expecting the output in a structured JSON format for automated downstream processing. <br \/>\r<br>Which AI_COMPLETE configuration best addresses their new requirement while considering cost-efficiency and output reliability?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='441534' \/><input type='hidden' id='answerType441534' 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-441534[]' id='answer-id-1708261' class='answer   answerof-441534 ' value='1708261'   \/><label for='answer-id-1708261' id='answer-label-1708261' class=' answer'><span><br><img decoding=\"async\" width=622 height=19 id=\"Picture 237\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image227.jpg\"><br>\r\nThis uses a smaller model and a structured output schema to ensure JSON adherence.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441534[]' id='answer-id-1708262' class='answer   answerof-441534 ' value='1708262'   \/><label for='answer-id-1708262' id='answer-label-1708262' class=' answer'><span><br><img decoding=\"async\" width=623 height=45 id=\"Picture 238\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image228.jpg\"><br>\r\nThis leverages a more capable model, explicit 'Respond in JSON' prompt, and detailed schema with required fields, alongside a recommended temperature of 0 for consistency.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441534[]' id='answer-id-1708263' class='answer   answerof-441534 ' value='1708263'   \/><label for='answer-id-1708263' id='answer-label-1708263' class=' answer'><span><br><img decoding=\"async\" width=623 height=24 id=\"Picture 239\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image229.jpg\"><br>\r\nThis uses a medium model with high temperature for diverse output, potentially reducing reliability.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441534[]' id='answer-id-1708264' class='answer   answerof-441534 ' value='1708264'   \/><label for='answer-id-1708264' id='answer-label-1708264' class=' answer'><span><br><img decoding=\"async\" width=623 height=23 id=\"Picture 240\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image230.jpg\"><br>\r\nThis uses a powerful model with Cortex Guard enabled for safety, but without explicitly guiding JSON output for complex tasks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441534[]' id='answer-id-1708265' class='answer   answerof-441534 ' value='1708265'   \/><label for='answer-id-1708265' id='answer-label-1708265' class=' answer'><span><br><img decoding=\"async\" width=623 height=23 id=\"Picture 242\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image231.jpg\"><br>\r\nThis leverages the classification function to categorize detailed sentiment, but it does not produce a structured JSON output with multiple sentiment categories for a single input.<\/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-441535'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. <br \/>\r<br>Which set of commands correctly demonstrates tagging the local image and pushing it to the repository? <br \/>\r<br><br><img decoding=\"async\" width=624 height=181 id=\"Picture 620\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image071-7.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_22' value='441535' \/><input type='hidden' id='answerType441535' 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-441535[]' id='answer-id-1708266' class='answer   answerof-441535 ' value='1708266'   \/><label for='answer-id-1708266' id='answer-label-1708266' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441535[]' id='answer-id-1708267' class='answer   answerof-441535 ' value='1708267'   \/><label for='answer-id-1708267' id='answer-label-1708267' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441535[]' id='answer-id-1708268' class='answer   answerof-441535 ' value='1708268'   \/><label for='answer-id-1708268' id='answer-label-1708268' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441535[]' id='answer-id-1708269' class='answer   answerof-441535 ' value='1708269'   \/><label for='answer-id-1708269' id='answer-label-1708269' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441535[]' id='answer-id-1708270' class='answer   answerof-441535 ' value='1708270'   \/><label for='answer-id-1708270' id='answer-label-1708270' class=' answer'><span>Option E<\/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-441536'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A data engineer is building a Snowflake data pipeline to ingest customer reviews from a raw staging table into a processed table. For each review, they need to determine the overall sentiment (positive, neutral, negative) and store this as a distinct column. The pipeline is implemented using SQL with streams and tasks to process new data. <br \/>\r<br>Which Snowflake Cortex LLM function, when integrated into the SQL task, is best suited for this sentiment classification and ensures a structured, single-label output for each review? <br \/>\r<br><br><img decoding=\"async\" width=651 height=125 id=\"\u56fe\u7247 1\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image009-18.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_23' value='441536' \/><input type='hidden' id='answerType441536' 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-441536[]' id='answer-id-1708271' class='answer   answerof-441536 ' value='1708271'   \/><label for='answer-id-1708271' id='answer-label-1708271' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441536[]' id='answer-id-1708272' class='answer   answerof-441536 ' value='1708272'   \/><label for='answer-id-1708272' id='answer-label-1708272' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441536[]' id='answer-id-1708273' class='answer   answerof-441536 ' value='1708273'   \/><label for='answer-id-1708273' id='answer-label-1708273' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441536[]' id='answer-id-1708274' class='answer   answerof-441536 ' value='1708274'   \/><label for='answer-id-1708274' id='answer-label-1708274' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441536[]' id='answer-id-1708275' class='answer   answerof-441536 ' value='1708275'   \/><label for='answer-id-1708275' id='answer-label-1708275' class=' answer'><span>Option E<\/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-441537'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a \u2018custom_model.ModelContext'. <br \/>\r<br>Which of the following statements correctly describe the considerations for the call and the model's environment? <br \/>\r<br><br><img decoding=\"async\" width=625 height=131 id=\"Picture 645\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image078-5.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_24' value='441537' \/><input type='hidden' id='answerType441537' 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-441537[]' id='answer-id-1708276' class='answer   answerof-441537 ' value='1708276'   \/><label for='answer-id-1708276' id='answer-label-1708276' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441537[]' id='answer-id-1708277' class='answer   answerof-441537 ' value='1708277'   \/><label for='answer-id-1708277' id='answer-label-1708277' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441537[]' id='answer-id-1708278' class='answer   answerof-441537 ' value='1708278'   \/><label for='answer-id-1708278' id='answer-label-1708278' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441537[]' id='answer-id-1708279' class='answer   answerof-441537 ' value='1708279'   \/><label for='answer-id-1708279' id='answer-label-1708279' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441537[]' id='answer-id-1708280' class='answer   answerof-441537 ' value='1708280'   \/><label for='answer-id-1708280' id='answer-label-1708280' class=' answer'><span>Option E<\/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-441538'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>A team is designing a complex Gen AI application in Snowflake, which includes components for training a custom LLM, running batch inference, and providing a real-time conversational interface. They plan to leverage Snowpark Container Services (SPCS) for these workloads. <br \/>\r<br>Which of the following statements accurately describe the suitable SPCS service design models and important considerations for these different application components? (Select all that apply.)<\/div><input type='hidden' name='question_id[]' id='qID_25' value='441538' \/><input type='hidden' id='answerType441538' 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-441538[]' id='answer-id-1708281' class='answer   answerof-441538 ' value='1708281'   \/><label for='answer-id-1708281' id='answer-label-1708281' class=' answer'><span>GPU-accelerated LLM training, which is a finite and often resource-intensive task, is best implemented as a \u2018\u2018job\u2019\u2019 in SPCS, invoked via &quot;EXECUTE JOB SERVICE', as it is designed to run to completion and then spin down.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441538[]' id='answer-id-1708282' class='answer   answerof-441538 ' value='1708282'   \/><label for='answer-id-1708282' id='answer-label-1708282' class=' answer'><span>Real-time LLM inference for a conversational interface is ideally deployed as a \u2018 \u2018Service\u2019 \u2018 in SPCS, which is long-running and accessible via an HTTP endpoint, ensuring continuous availability and responsiveness.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441538[]' id='answer-id-1708283' class='answer   answerof-441538 ' value='1708283'   \/><label for='answer-id-1708283' id='answer-label-1708283' class=' answer'><span>For batch inference on Snowflake data where data locality and efficiency are key, using \u2018\u2018Service Functions\u2019\u2019 is highly efficient because data is passed as input parameters directly from SQL queries, and this design ensures the data never leaves the Snowflake network boundary.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441538[]' id='answer-id-1708284' class='answer   answerof-441538 ' value='1708284'   \/><label for='answer-id-1708284' id='answer-label-1708284' class=' answer'><span>When deploying LLMs to SPCS, it's generally most cost-efficient to use generic CPU instance types like 'CPU X64 XS' for all tasks, as GPU instances (e.g., are exclusively for highly specialized computer vision tasks and not optimized for LLMs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441538[]' id='answer-id-1708285' class='answer   answerof-441538 ' value='1708285'   \/><label for='answer-id-1708285' id='answer-label-1708285' class=' answer'><span>Container images for SPCS deployments are typically pushed to a public Docker Hub repository, and Snowflake pulls them as needed during service creation and scaling, simplifying image management.<\/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-441539'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>A Snowflake developer, named ANALYST USER, is tasked with creating a Streamlit in Snowflake (SiS) application that will utilize both SNOWFLAKE. CORTEX. COMPLETE for generating responses and SNOWFLAKE. CORTEX.CLASSIFY_TEXT for categorizing user input. <br \/>\r<br>To ensure the role used by ANALYST USER has the necessary permissions for executing these Cortex LLM functions and operating within a specified database and schema, which of the following database roles or privileges must be granted? (Select all that apply.)<\/div><input type='hidden' name='question_id[]' id='qID_26' value='441539' \/><input type='hidden' id='answerType441539' 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-441539[]' id='answer-id-1708286' class='answer   answerof-441539 ' value='1708286'   \/><label for='answer-id-1708286' id='answer-label-1708286' class=' answer'><span><br><img decoding=\"async\" width=624 height=17 id=\"Picture 356\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image271.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441539[]' id='answer-id-1708287' class='answer   answerof-441539 ' value='1708287'   \/><label for='answer-id-1708287' id='answer-label-1708287' class=' answer'><span><br><img decoding=\"async\" width=624 height=15 id=\"Picture 359\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image272.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441539[]' id='answer-id-1708288' class='answer   answerof-441539 ' value='1708288'   \/><label for='answer-id-1708288' id='answer-label-1708288' class=' answer'><span>The USAGE privilege on the database and schema where the Streamlit application runs and potentially stores related data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441539[]' id='answer-id-1708289' class='answer   answerof-441539 ' value='1708289'   \/><label for='answer-id-1708289' id='answer-label-1708289' class=' answer'><span><br><img decoding=\"async\" width=397 height=22 id=\"Picture 362\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image273.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441539[]' id='answer-id-1708290' class='answer   answerof-441539 ' value='1708290'   \/><label for='answer-id-1708290' id='answer-label-1708290' class=' answer'><span><br><img decoding=\"async\" width=620 height=27 id=\"Picture 365\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image274.jpg\"><br><\/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-441540'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>A data engineer is tasked with establishing a robust MLOps pipeline using the Snowflake Model Registry. They have trained a scikit-learn model and need to log it. <br \/>\r<br>Which of the following statements correctly describes a \u2018required\u2019 step or privilege for successfully logging a model using the 'Registry.log_model' method? <br \/>\r<br><br><img decoding=\"async\" width=624 height=123 id=\"Picture 649\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image080-6.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_27' value='441540' \/><input type='hidden' id='answerType441540' 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-441540[]' id='answer-id-1708291' class='answer   answerof-441540 ' value='1708291'   \/><label for='answer-id-1708291' id='answer-label-1708291' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441540[]' id='answer-id-1708292' class='answer   answerof-441540 ' value='1708292'   \/><label for='answer-id-1708292' id='answer-label-1708292' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441540[]' id='answer-id-1708293' class='answer   answerof-441540 ' value='1708293'   \/><label for='answer-id-1708293' id='answer-label-1708293' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441540[]' id='answer-id-1708294' class='answer   answerof-441540 ' value='1708294'   \/><label for='answer-id-1708294' id='answer-label-1708294' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441540[]' id='answer-id-1708295' class='answer   answerof-441540 ' value='1708295'   \/><label for='answer-id-1708295' id='answer-label-1708295' class=' answer'><span>Option E<\/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-441541'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role \u2018DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet\u2019 model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=624 height=73 id=\"Picture 699\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image098-5.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=624 height=62 id=\"Picture 702\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image099-5.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=624 height=62 id=\"Picture 705\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image100-5.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=624 height=72 id=\"Picture 708\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image101-7.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=556 height=75 id=\"Picture 711\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image102-6.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_28' value='441541' \/><input type='hidden' id='answerType441541' 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-441541[]' id='answer-id-1708296' class='answer   answerof-441541 ' value='1708296'   \/><label for='answer-id-1708296' id='answer-label-1708296' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441541[]' id='answer-id-1708297' class='answer   answerof-441541 ' value='1708297'   \/><label for='answer-id-1708297' id='answer-label-1708297' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441541[]' id='answer-id-1708298' class='answer   answerof-441541 ' value='1708298'   \/><label for='answer-id-1708298' id='answer-label-1708298' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441541[]' id='answer-id-1708299' class='answer   answerof-441541 ' value='1708299'   \/><label for='answer-id-1708299' id='answer-label-1708299' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441541[]' id='answer-id-1708300' class='answer   answerof-441541 ' value='1708300'   \/><label for='answer-id-1708300' id='answer-label-1708300' class=' answer'><span>Option E<\/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-441542'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>A Data Engineer is responsible for deploying machine learning models using Snowpark Container Services. They need to ensure that a specific role, model_deployer_role, has the appropriate permissions to create a Snowpark Container Service that uses an image from an existing image repository named my_inferenc_ images. <br \/>\r<br>Which of the following SQL commands grant the necessary privileges 'on the image repository\u2019 for this purpose? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=504 height=21 id=\"Picture 624\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image073-6.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=624 height=18 id=\"Picture 627\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image074-7.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=624 height=20 id=\"Picture 630\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image075-8.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=624 height=21 id=\"Picture 639\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image076-8.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=624 height=14 id=\"Picture 642\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image077-7.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_29' value='441542' \/><input type='hidden' id='answerType441542' 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-441542[]' id='answer-id-1708301' class='answer   answerof-441542 ' value='1708301'   \/><label for='answer-id-1708301' id='answer-label-1708301' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441542[]' id='answer-id-1708302' class='answer   answerof-441542 ' value='1708302'   \/><label for='answer-id-1708302' id='answer-label-1708302' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441542[]' id='answer-id-1708303' class='answer   answerof-441542 ' value='1708303'   \/><label for='answer-id-1708303' id='answer-label-1708303' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441542[]' id='answer-id-1708304' class='answer   answerof-441542 ' value='1708304'   \/><label for='answer-id-1708304' id='answer-label-1708304' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441542[]' id='answer-id-1708305' class='answer   answerof-441542 ' value='1708305'   \/><label for='answer-id-1708305' id='answer-label-1708305' class=' answer'><span>Option E<\/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-441543'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>An ML engineer is planning a fine-tuning project for a llama3.1-8b model to summarize long customer support tickets. They are considering the impact of dataset size and max_epochs on cost and performance, as well as the behavior of the fine-tuned model for inference. <br \/>\r<br>Which statements about cost and performance in Snowflake Cortex Fine-tuning are true? (Select all that apply)<\/div><input type='hidden' name='question_id[]' id='qID_30' value='441543' \/><input type='hidden' id='answerType441543' 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-441543[]' id='answer-id-1708306' class='answer   answerof-441543 ' value='1708306'   \/><label for='answer-id-1708306' id='answer-label-1708306' class=' answer'><span>D When fine-tuning a llama3.1-8b model, the maximum input context (for the prompt ) is 20,000 tokens, and the maximum output context (for the completion) is 4,000 tokens.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441543[]' id='answer-id-1708307' class='answer   answerof-441543 ' value='1708307'   \/><label for='answer-id-1708307' id='answer-label-1708307' class=' answer'><span>The compute cost for fine-tuning is primarily determined by multiplying the number of input tokens in the training data by the number of epochs trained.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441543[]' id='answer-id-1708308' class='answer   answerof-441543 ' value='1708308'   \/><label for='answer-id-1708308' id='answer-label-1708308' class=' answer'><span>For optimal cost efficiency, especially with smaller datasets, the max_epochs parameter should be consistently set to its maximum allowed value of 10 to ensure the best model performance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441543[]' id='answer-id-1708309' class='answer   answerof-441543 ' value='1708309'   \/><label for='answer-id-1708309' id='answer-label-1708309' class=' answer'><span>The cost for inferencing with a fine-tuned model using the COMPLETE function is solely based on the number of output tokens generated by the model, as input token costs are absorbed during fine-tuning.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441543[]' id='answer-id-1708310' class='answer   answerof-441543 ' value='1708310'   \/><label for='answer-id-1708310' id='answer-label-1708310' class=' answer'><span>For large fine-tuning jobs with substantial datasets, particularly when exceeding millions of rows, utilizing Snowpark-optimized warehouses is recommended for improved performance during the training phase.<\/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-441544'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated.<br \/>\r\n<br \/>\r\nWhat actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)<br \/>\r\n<br \/>\r\n<br \/>\r\n<img loading=\"lazy\" decoding=\"async\" id=\"Picture 419\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image013-17.jpg\" width=\"624\" height=\"145\" \/><\/div><input type='hidden' name='question_id[]' id='qID_31' value='441544' \/><input type='hidden' id='answerType441544' 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-441544[]' id='answer-id-1708311' class='answer   answerof-441544 ' value='1708311'   \/><label for='answer-id-1708311' id='answer-label-1708311' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441544[]' id='answer-id-1709294' class='answer   answerof-441544 ' value='1709294'   \/><label for='answer-id-1709294' id='answer-label-1709294' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441544[]' id='answer-id-1709295' class='answer   answerof-441544 ' value='1709295'   \/><label for='answer-id-1709295' id='answer-label-1709295' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441544[]' id='answer-id-1709296' class='answer   answerof-441544 ' value='1709296'   \/><label for='answer-id-1709296' id='answer-label-1709296' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441544[]' id='answer-id-1709297' class='answer   answerof-441544 ' value='1709297'   \/><label for='answer-id-1709297' id='answer-label-1709297' class=' answer'><span>Option E<\/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-441545'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch', 'transformerS, 'opencv-python'). The scenario dictates that \u2018opencv-python' is only available via PyPI, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPI. The engineer uses the Snowflake Model Registry to log the model. <br \/>\r<br>Which of the following 'log model' and 'create_service' configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=560 height=302 id=\"Picture 596\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image065-7.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=623 height=206 id=\"Picture 605\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image066-7.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=565 height=304 id=\"Picture 608\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image067-7.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=593 height=306 id=\"Picture 611\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image068-7.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=624 height=318 id=\"Picture 614\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image069-6.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_32' value='441545' \/><input type='hidden' id='answerType441545' 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-441545[]' id='answer-id-1708312' class='answer   answerof-441545 ' value='1708312'   \/><label for='answer-id-1708312' id='answer-label-1708312' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441545[]' id='answer-id-1708313' class='answer   answerof-441545 ' value='1708313'   \/><label for='answer-id-1708313' id='answer-label-1708313' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441545[]' id='answer-id-1708314' class='answer   answerof-441545 ' value='1708314'   \/><label for='answer-id-1708314' id='answer-label-1708314' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441545[]' id='answer-id-1708315' class='answer   answerof-441545 ' value='1708315'   \/><label for='answer-id-1708315' id='answer-label-1708315' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441545[]' id='answer-id-1708316' class='answer   answerof-441545 ' value='1708316'   \/><label for='answer-id-1708316' id='answer-label-1708316' class=' answer'><span>Option E<\/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-441546'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. <br \/>\r<br>Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement? <br \/>\r<br><br><img decoding=\"async\" width=643 height=109 id=\"\u56fe\u7247 6\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image002-28.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_33' value='441546' \/><input type='hidden' id='answerType441546' 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-441546[]' id='answer-id-1708317' class='answer   answerof-441546 ' value='1708317'   \/><label for='answer-id-1708317' id='answer-label-1708317' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441546[]' id='answer-id-1708318' class='answer   answerof-441546 ' value='1708318'   \/><label for='answer-id-1708318' id='answer-label-1708318' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441546[]' id='answer-id-1708319' class='answer   answerof-441546 ' value='1708319'   \/><label for='answer-id-1708319' id='answer-label-1708319' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441546[]' id='answer-id-1708320' class='answer   answerof-441546 ' value='1708320'   \/><label for='answer-id-1708320' id='answer-label-1708320' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441546[]' id='answer-id-1708321' class='answer   answerof-441546 ' value='1708321'   \/><label for='answer-id-1708321' id='answer-label-1708321' class=' answer'><span>Option E<\/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-441547'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>A development team is building a RAG application in Snowflake Cortex that needs to extract high-fidelity text and layout from a collection of technical documentation PDFs stored in an internal stage to power semantic search and LLM responses. They want to ensure proper context retrieval for complex user queries. <br \/>\r<br>Given this scenario, which of the following actions or statements are crucial for effectively leveraging AI_PARSE_DOCUMENT to optimize the RAG pipeline? <br \/>\r<br><br><img decoding=\"async\" width=625 height=149 id=\"Picture 260\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image239.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_34' value='441547' \/><input type='hidden' id='answerType441547' 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-441547[]' id='answer-id-1708322' class='answer   answerof-441547 ' value='1708322'   \/><label for='answer-id-1708322' id='answer-label-1708322' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441547[]' id='answer-id-1708323' class='answer   answerof-441547 ' value='1708323'   \/><label for='answer-id-1708323' id='answer-label-1708323' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441547[]' id='answer-id-1708324' class='answer   answerof-441547 ' value='1708324'   \/><label for='answer-id-1708324' id='answer-label-1708324' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441547[]' id='answer-id-1708325' class='answer   answerof-441547 ' value='1708325'   \/><label for='answer-id-1708325' id='answer-label-1708325' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441547[]' id='answer-id-1708326' class='answer   answerof-441547 ' value='1708326'   \/><label for='answer-id-1708326' id='answer-label-1708326' class=' answer'><span>Option E<\/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-441548'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. <br \/>\r<br>To enable users to interact with this agent via the agent: run API, which of the following database roles or privileges must be granted to their account role?<\/div><input type='hidden' name='question_id[]' id='qID_35' value='441548' \/><input type='hidden' id='answerType441548' 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-441548[]' id='answer-id-1708327' class='answer   answerof-441548 ' value='1708327'   \/><label for='answer-id-1708327' id='answer-label-1708327' class=' answer'><span>CREATE EXTERNAL AGENT privilege on the schema where the agent is defined.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708328' class='answer   answerof-441548 ' value='1708328'   \/><label for='answer-id-1708328' id='answer-label-1708328' class=' answer'><span>The SNOWFLAK<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708329' class='answer   answerof-441548 ' value='1708329'   \/><label for='answer-id-1708329' id='answer-label-1708329' class=' answer'><span>CORTEX AGENT USER database role, which specifically provides access to the Agents feature.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708330' class='answer   answerof-441548 ' value='1708330'   \/><label for='answer-id-1708330' id='answer-label-1708330' class=' answer'><span>The SNOWFLAK<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708331' class='answer   answerof-441548 ' value='1708331'   \/><label for='answer-id-1708331' id='answer-label-1708331' class=' answer'><span>DOCUMENT INTELLIGENCE CREATOR database role, especially if the agent processes unstructured documents.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708332' class='answer   answerof-441548 ' value='1708332'   \/><label for='answer-id-1708332' id='answer-label-1708332' class=' answer'><span>The SNOWFLAK<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708333' class='answer   answerof-441548 ' value='1708333'   \/><label for='answer-id-1708333' id='answer-label-1708333' class=' answer'><span>DOCUMENT INTELLIGENCE CREATOR database role, especially if the agent processes unstructured documents.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441548[]' id='answer-id-1708334' class='answer   answerof-441548 ' value='1708334'   \/><label for='answer-id-1708334' id='answer-label-1708334' class=' answer'><span>The ACCOUNTADMIN role, as Cortex Agents are a Preview Feature and require elevated privileges for runtime.<\/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-441549'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. <br \/>\r<br>Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply) <br \/>\r<br><br><img decoding=\"async\" width=623 height=275 id=\"Picture 349\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image268.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_36' value='441549' \/><input type='hidden' id='answerType441549' 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-441549[]' id='answer-id-1708335' class='answer   answerof-441549 ' value='1708335'   \/><label for='answer-id-1708335' id='answer-label-1708335' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441549[]' id='answer-id-1708336' class='answer   answerof-441549 ' value='1708336'   \/><label for='answer-id-1708336' id='answer-label-1708336' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441549[]' id='answer-id-1708337' class='answer   answerof-441549 ' value='1708337'   \/><label for='answer-id-1708337' id='answer-label-1708337' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441549[]' id='answer-id-1708338' class='answer   answerof-441549 ' value='1708338'   \/><label for='answer-id-1708338' id='answer-label-1708338' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441549[]' id='answer-id-1708339' class='answer   answerof-441549 ' value='1708339'   \/><label for='answer-id-1708339' id='answer-label-1708339' class=' answer'><span>Option E<\/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-441550'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>A Snowflake account is located in the AWS US East 1 (N. Virginia) region. The 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to &quot;mistral-7b&quot; and 'CORTEX ENABLED CROSS REGION' to 'ANY REGION'. A data scientist, whose role has only the 'SNOWFLAKE.CORTEX USER database role, performs several 'AI COMPLETE calls. <br \/>\r<br>Which of the following statements correctly describe the behavior of these calls under the given configuration? <br \/>\r<br><br><img decoding=\"async\" width=624 height=133 id=\"Picture 819\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image138-4.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_37' value='441550' \/><input type='hidden' id='answerType441550' 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-441550[]' id='answer-id-1708340' class='answer   answerof-441550 ' value='1708340'   \/><label for='answer-id-1708340' id='answer-label-1708340' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441550[]' id='answer-id-1708341' class='answer   answerof-441550 ' value='1708341'   \/><label for='answer-id-1708341' id='answer-label-1708341' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441550[]' id='answer-id-1708342' class='answer   answerof-441550 ' value='1708342'   \/><label for='answer-id-1708342' id='answer-label-1708342' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441550[]' id='answer-id-1708343' class='answer   answerof-441550 ' value='1708343'   \/><label for='answer-id-1708343' id='answer-label-1708343' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441550[]' id='answer-id-1708344' class='answer   answerof-441550 ' value='1708344'   \/><label for='answer-id-1708344' id='answer-label-1708344' class=' answer'><span>Option E<\/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-441551'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>An ML engineer is preparing a Docker image for a custom LLM application that will be deployed to Snowpark Container Services (SPCS). The application uses a mix of packages, some commonly found in the Snowflake Anaconda channel and others from general open-source repositories like PyPI. They have the following Docker-file snippet and need to ensure the dependencies are correctly installed for the SPCS environment to support a GPU workload. <br \/>\r<br>Which of the following approaches for installing Python packages in the Dockerfile would ensure a robust and compatible setup for a custom LLM running in Snowpark Container Services, based on best practices for managing dependencies in this environment? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=621 height=19 id=\"Picture 511\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image040-8.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=624 height=23 id=\"Picture 512\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image041-7.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=624 height=25 id=\"Picture 515\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image042-8.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=623 height=27 id=\"Picture 518\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image043-7.jpg\"><br><br \/>\r<br>E) <br \/>\r<br><br><img decoding=\"async\" width=624 height=38 id=\"Picture 527\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image044-7.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_38' value='441551' \/><input type='hidden' id='answerType441551' 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-441551[]' id='answer-id-1708345' class='answer   answerof-441551 ' value='1708345'   \/><label for='answer-id-1708345' id='answer-label-1708345' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441551[]' id='answer-id-1708346' class='answer   answerof-441551 ' value='1708346'   \/><label for='answer-id-1708346' id='answer-label-1708346' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441551[]' id='answer-id-1708347' class='answer   answerof-441551 ' value='1708347'   \/><label for='answer-id-1708347' id='answer-label-1708347' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441551[]' id='answer-id-1708348' class='answer   answerof-441551 ' value='1708348'   \/><label for='answer-id-1708348' id='answer-label-1708348' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-441551[]' id='answer-id-1708349' class='answer   answerof-441551 ' value='1708349'   \/><label for='answer-id-1708349' id='answer-label-1708349' class=' answer'><span>Option E<\/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-441552'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>A financial data team is implementing a Snowflake Cortex AI solution to summarize regulatory documents using SNOWFLAKE.CORTEX.TRY_COMPLETE <br \/>\r<br>They aim for both cost efficiency and high reliability, especially when dealing with documents that might occasionally exceed model context limits or result in malformed output. <br \/>\r<br>Which of the following statements about the cost and operational behavior of TRY_COMPLETE <br \/>\r<br>are TRUE in this context? (Select all that apply) <br \/>\r<br><br><img decoding=\"async\" width=623 height=335 id=\"Picture 191\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image211.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_39' value='441552' \/><input type='hidden' id='answerType441552' 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-441552[]' id='answer-id-1708350' class='answer   answerof-441552 ' value='1708350'   \/><label for='answer-id-1708350' id='answer-label-1708350' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441552[]' id='answer-id-1708351' class='answer   answerof-441552 ' value='1708351'   \/><label for='answer-id-1708351' id='answer-label-1708351' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441552[]' id='answer-id-1708352' class='answer   answerof-441552 ' value='1708352'   \/><label for='answer-id-1708352' id='answer-label-1708352' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441552[]' id='answer-id-1708353' class='answer   answerof-441552 ' value='1708353'   \/><label for='answer-id-1708353' id='answer-label-1708353' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441552[]' id='answer-id-1708354' class='answer   answerof-441552 ' value='1708354'   \/><label for='answer-id-1708354' id='answer-label-1708354' class=' answer'><span>Option E<\/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-441553'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span><br><img decoding=\"async\" width=623 height=76 id=\"Picture 398\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image006-24.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_40' value='441553' \/><input type='hidden' id='answerType441553' 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-441553[]' id='answer-id-1708355' class='answer   answerof-441553 ' value='1708355'   \/><label for='answer-id-1708355' id='answer-label-1708355' class=' answer'><span><br><img decoding=\"async\" width=649 height=15 id=\"\u56fe\u7247 3\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image007-23.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441553[]' id='answer-id-1708356' class='answer   answerof-441553 ' value='1708356'   \/><label for='answer-id-1708356' id='answer-label-1708356' class=' answer'><span><br><img decoding=\"async\" width=649 height=15 id=\"\u56fe\u7247 2\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/11\/image008-20.jpg\"><br><\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441553[]' id='answer-id-1708357' class='answer   answerof-441553 ' value='1708357'   \/><label for='answer-id-1708357' id='answer-label-1708357' class=' answer'><span>The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441553[]' id='answer-id-1708358' class='answer   answerof-441553 ' value='1708358'   \/><label for='answer-id-1708358' id='answer-label-1708358' class=' answer'><span>The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-441553[]' id='answer-id-1708359' class='answer   answerof-441553 ' value='1708359'   \/><label for='answer-id-1708359' id='answer-label-1708359' class=' answer'><span>The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-41'>\n\t<div class='question-content'>\n\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading...\" title=\"Loading...\" \/>&nbsp;Loading...\t<\/div>\n<\/div>\n\n<br \/>\n\t\n\t\t\t<div class=\"watupro_buttons flex \" id=\"watuPROButtons11226\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; Previous\" onclick=\"WatuPRO.nextQuestion(event, 'previous');\"\/><\/div>\t\t  \t\t  \t\t   \n\t\t   \t  \t\t<div><input type=\"button\" name=\"action\" class=\"watupro-submit-button\" onclick=\"WatuPRO.submitResult(event)\" id=\"action-button\" value=\"View Results\"  \/>\n\t\t<\/div>\n\t\t<\/div>\n\t\t\n\t<input type=\"hidden\" name=\"quiz_id\" value=\"11226\" id=\"watuPROExamID\"\/>\n\t<input type=\"hidden\" name=\"start_time\" id=\"startTime\" value=\"2026-04-13 00:17:48\" \/>\n\t<input type=\"hidden\" name=\"start_timestamp\" id=\"startTimeStamp\" value=\"1776039468\" \/>\n\t<input type=\"hidden\" name=\"question_ids\" value=\"\" \/>\n\t<input type=\"hidden\" name=\"watupro_questions\" value=\"441479:1708076,1708077,1708078,1708079,1708080 | 441480:1708081,1708082,1708083,1708084,1708085 | 441481:1708086,1708087,1708088,1708089,1708090 | 441484:1708096,1708097,1708098,1708099,1708100 | 441485:1708101,1708102,1708103,1708104,1708105 | 441488:1708108,1708109,1708110,1708111,1708112 | 441494:1708126,1708128,1708130,1708131,1708133 | 441498:1708143,1708144,1708146,1708148,1708150 | 441501:1708155,1708156,1708157,1708158,1708159,1708160,1708161 | 441504:1708169,1708170,1708171,1708172,1708173 | 441508:1708181,1709290,1709291,1709292,1709293 | 441509:1708182,1708183,1708184,1708185,1708186 | 441513:1708191,1708192,1708193,1708194,1708195 | 441516:1708198,1708201,1708202,1708204,1708206 | 441521:1708211,1708212,1708213,1708214,1708215 | 441522:1708217,1708218,1708220,1708222,1708224 | 441530:1708241,1708242,1708243,1708244,1708245 | 441531:1708246,1708247,1708248,1708249,1708250 | 441532:1708251,1708252,1708253,1708254,1708255 | 441533:1708256,1708257,1708258,1708259,1708260 | 441534:1708261,1708262,1708263,1708264,1708265 | 441535:1708266,1708267,1708268,1708269,1708270 | 441536:1708271,1708272,1708273,1708274,1708275 | 441537:1708276,1708277,1708278,1708279,1708280 | 441538:1708281,1708282,1708283,1708284,1708285 | 441539:1708286,1708287,1708288,1708289,1708290 | 441540:1708291,1708292,1708293,1708294,1708295 | 441541:1708296,1708297,1708298,1708299,1708300 | 441542:1708301,1708302,1708303,1708304,1708305 | 441543:1708306,1708307,1708308,1708309,1708310 | 441544:1708311,1709294,1709295,1709296,1709297 | 441545:1708312,1708313,1708314,1708315,1708316 | 441546:1708317,1708318,1708319,1708320,1708321 | 441547:1708322,1708323,1708324,1708325,1708326 | 441548:1708327,1708328,1708329,1708330,1708331,1708332,1708333,1708334 | 441549:1708335,1708336,1708337,1708338,1708339 | 441550:1708340,1708341,1708342,1708343,1708344 | 441551:1708345,1708346,1708347,1708348,1708349 | 441552:1708350,1708351,1708352,1708353,1708354 | 441553:1708355,1708356,1708357,1708358,1708359\" \/>\n\t<input type=\"hidden\" name=\"no_ajax\" value=\"0\">\t\t\t<\/form>\n\t<p>&nbsp;<\/p>\n<\/div>\n\n<script type=\"text\/javascript\">\n\/\/jQuery(document).ready(function(){\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \t\nvar question_ids = \"441479,441480,441481,441484,441485,441488,441494,441498,441501,441504,441508,441509,441513,441516,441521,441522,441530,441531,441532,441533,441534,441535,441536,441537,441538,441539,441540,441541,441542,441543,441544,441545,441546,441547,441548,441549,441550,441551,441552,441553\";\nWatuPROSettings[11226] = {};\nWatuPRO.qArr = question_ids.split(',');\nWatuPRO.exam_id = 11226;\t    \nWatuPRO.post_id = 115128;\nWatuPRO.store_progress = 0;\nWatuPRO.curCatPage = 1;\nWatuPRO.requiredIDs=\"0\".split(\",\");\nWatuPRO.hAppID = \"0.69942600 1776039468\";\nvar url = \"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/show_exam.php\";\nWatuPRO.examMode = 1;\nWatuPRO.siteURL=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-admin\/admin-ajax.php\";\nWatuPRO.emailIsNotRequired = 0;\nWatuPROIntel.init(11226);\nWatuPRO.inCategoryPages=1;});    \t \n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>The SnowPro Specialty: Gen AI (GES-C01) is available to validate specialized knowledge, skills, and best practices for leveraging Gen AI methodologies in Snowflake, including key concepts, features, and programming constructs. During your GES-C01 exam preparation, you can choose real GES-C01 dumps (V8.02) from DumpsBase. We have the GES-C01 exam questions with verified answers that help [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11847,20400],"tags":[20402,20401],"class_list":["post-115128","post","type-post","status-publish","format-standard","hentry","category-snowflake","category-snowpro-specialist","tag-ges-c01-dumps","tag-snowpro-specialty-gen-ai-ges-c01"],"_links":{"self":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/115128","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/comments?post=115128"}],"version-history":[{"count":1,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/115128\/revisions"}],"predecessor-version":[{"id":115129,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/posts\/115128\/revisions\/115129"}],"wp:attachment":[{"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/media?parent=115128"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/categories?post=115128"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dumpsbase.com\/freedumps\/wp-json\/wp\/v2\/tags?post=115128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}