{"id":105716,"date":"2025-07-15T07:13:27","date_gmt":"2025-07-15T07:13:27","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=105716"},"modified":"2025-07-15T07:13:27","modified_gmt":"2025-07-15T07:13:27","slug":"databricks-generative-ai-engineer-associate-dumps-updated-to-v9-02-with-61qas-helping-you-build-confidence","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/databricks-generative-ai-engineer-associate-dumps-updated-to-v9-02-with-61qas-helping-you-build-confidence.html","title":{"rendered":"Databricks Generative AI Engineer Associate Dumps Updated to V9.02 with 61Q&#038;As: Helping You Build Confidence"},"content":{"rendered":"<p>At DumpsBase, you can complete the Databricks Certified Generative AI Engineer Associate certification exam by learning the most current Databricks Generative AI Engineer Associate dumps. We have updated these dumps to V9.02, offering you 61 exam questions and answers, which are completely aligned with Databricks\u2019 latest exam objectives and question formats. All those updated questions have been reviewed and validated by certified professionals with extensive experience in designing and implementing LLM-enabled solutions using <a href=\"https:\/\/www.dumpsbase.com\/databricks.html\"><em><strong>Databricks<\/strong><\/em><\/a>. Practicing with the most current questions will help you build confidence and reduce test anxiety.<\/p>\n<h2><span style=\"background-color: #00ffff;\"><em>Databricks Generative AI Engineer Associate free dumps are below<\/em><\/span> for reading first:<\/h2>\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam10434\" 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-10434\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-10434\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-413323'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author\u2019s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user\u2019s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values. <br \/>\r<br>Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_1' value='413323' \/><input type='hidden' id='answerType413323' 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-413323[]' id='answer-id-1601787' class='answer   answerof-413323 ' value='1601787'   \/><label for='answer-id-1601787' id='answer-label-1601787' class=' answer'><span>Change embedding models and compare performance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413323[]' id='answer-id-1601788' class='answer   answerof-413323 ' value='1601788'   \/><label for='answer-id-1601788' id='answer-label-1601788' class=' answer'><span>Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413323[]' id='answer-id-1601789' class='answer   answerof-413323 ' value='1601789'   \/><label for='answer-id-1601789' id='answer-label-1601789' class=' answer'><span>Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters. Choose the strategy that gives the best performance metric.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413323[]' id='answer-id-1601790' class='answer   answerof-413323 ' value='1601790'   \/><label for='answer-id-1601790' id='answer-label-1601790' class=' answer'><span>Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413323[]' id='answer-id-1601791' class='answer   answerof-413323 ' value='1601791'   \/><label for='answer-id-1601791' id='answer-label-1601791' class=' answer'><span>Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.<\/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-413324'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport. <br \/>\r<br>What are the steps needed to build this RAG application and deploy it?<\/div><input type='hidden' name='question_id[]' id='qID_2' value='413324' \/><input type='hidden' id='answerType413324' 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-413324[]' id='answer-id-1601792' class='answer   answerof-413324 ' value='1601792'   \/><label for='answer-id-1601792' id='answer-label-1601792' class=' answer'><span>Ingest documents from a source C&gt; Index the documents and saves to Vector Search C&gt; User submits queries against an LLM C&gt; LLM retrieves relevant documents C&gt; Evaluate model C&gt; LLM generates a response C&gt; Deploy it using Model Serving<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413324[]' id='answer-id-1601793' class='answer   answerof-413324 ' value='1601793'   \/><label for='answer-id-1601793' id='answer-label-1601793' class=' answer'><span>Ingest documents from a source C&gt; Index the documents and save to Vector Search C&gt; User submits queries against an LLM C&gt; LLM retrieves relevant documents C&gt; LLM generates a response -&gt; Evaluate model C&gt; Deploy it using Model Serving<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413324[]' id='answer-id-1601794' class='answer   answerof-413324 ' value='1601794'   \/><label for='answer-id-1601794' id='answer-label-1601794' class=' answer'><span>Ingest documents from a source C&gt; Index the documents and save to Vector Search C&gt; Evaluate model C&gt; Deploy it using Model Serving<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413324[]' id='answer-id-1601795' class='answer   answerof-413324 ' value='1601795'   \/><label for='answer-id-1601795' id='answer-label-1601795' class=' answer'><span>User submits queries against an LLM C&gt; Ingest documents from a source C&gt; Index the documents and save to Vector Search C&gt; LLM retrieves relevant documents C&gt; LLM generates a response C&gt; Evaluate model C&gt; Deploy it using Model Serving<\/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-413325'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries. <br \/>\r<br>Which metric should they monitor for their customer service LLM application in production?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='413325' \/><input type='hidden' id='answerType413325' 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-413325[]' id='answer-id-1601796' class='answer   answerof-413325 ' value='1601796'   \/><label for='answer-id-1601796' id='answer-label-1601796' class=' answer'><span>Number of customer inquiries processed per unit of time<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413325[]' id='answer-id-1601797' class='answer   answerof-413325 ' value='1601797'   \/><label for='answer-id-1601797' id='answer-label-1601797' class=' answer'><span>Energy usage per query<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413325[]' id='answer-id-1601798' class='answer   answerof-413325 ' value='1601798'   \/><label for='answer-id-1601798' id='answer-label-1601798' class=' answer'><span>Final perplexity scores for the training of the model<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413325[]' id='answer-id-1601799' class='answer   answerof-413325 ' value='1601799'   \/><label for='answer-id-1601799' id='answer-label-1601799' class=' answer'><span>HuggingFace Leaderboard values for the base LLM<\/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-413326'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text. <br \/>\r<br>How should the Generative Al Engineer architect their system?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='413326' \/><input type='hidden' id='answerType413326' 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-413326[]' id='answer-id-1601800' class='answer   answerof-413326 ' value='1601800'   \/><label for='answer-id-1601800' id='answer-label-1601800' class=' answer'><span>Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413326[]' id='answer-id-1601801' class='answer   answerof-413326 ' value='1601801'   \/><label for='answer-id-1601801' id='answer-label-1601801' class=' answer'><span>Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members\u2019 profiles and perform keyword matching to find the best available team member.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413326[]' id='answer-id-1601802' class='answer   answerof-413326 ' value='1601802'   \/><label for='answer-id-1601802' id='answer-label-1601802' class=' answer'><span>Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413326[]' id='answer-id-1601803' class='answer   answerof-413326 ' value='1601803'   \/><label for='answer-id-1601803' id='answer-label-1601803' class=' answer'><span>Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.<\/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-413327'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles. <br \/>\r<br>Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='413327' \/><input type='hidden' id='answerType413327' 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-413327[]' id='answer-id-1601804' class='answer   answerof-413327 ' value='1601804'   \/><label for='answer-id-1601804' id='answer-label-1601804' class=' answer'><span>DatabrickslQ<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413327[]' id='answer-id-1601805' class='answer   answerof-413327 ' value='1601805'   \/><label for='answer-id-1601805' id='answer-label-1601805' class=' answer'><span>Foundation Model APIs<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413327[]' id='answer-id-1601806' class='answer   answerof-413327 ' value='1601806'   \/><label for='answer-id-1601806' id='answer-label-1601806' class=' answer'><span>Feature Serving<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413327[]' id='answer-id-1601807' class='answer   answerof-413327 ' value='1601807'   \/><label for='answer-id-1601807' id='answer-label-1601807' class=' answer'><span>AutoML<\/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-413328'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint\u2019s incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server. <br \/>\r<br>Which Databricks feature should they use instead which will perform the same task?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='413328' \/><input type='hidden' id='answerType413328' 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-413328[]' id='answer-id-1601808' class='answer   answerof-413328 ' value='1601808'   \/><label for='answer-id-1601808' id='answer-label-1601808' class=' answer'><span>Vector Search<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413328[]' id='answer-id-1601809' class='answer   answerof-413328 ' value='1601809'   \/><label for='answer-id-1601809' id='answer-label-1601809' class=' answer'><span>Lakeview<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413328[]' id='answer-id-1601810' class='answer   answerof-413328 ' value='1601810'   \/><label for='answer-id-1601810' id='answer-label-1601810' class=' answer'><span>DBSQL<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413328[]' id='answer-id-1601811' class='answer   answerof-413328 ' value='1601811'   \/><label for='answer-id-1601811' id='answer-label-1601811' class=' answer'><span>Inference Tables<\/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-413329'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs. <br \/>\r<br>Which action would be most effective in mitigating the problem of offensive text outputs?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='413329' \/><input type='hidden' id='answerType413329' 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-413329[]' id='answer-id-1601812' class='answer   answerof-413329 ' value='1601812'   \/><label for='answer-id-1601812' id='answer-label-1601812' class=' answer'><span>Increase the frequency of upstream data updates<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413329[]' id='answer-id-1601813' class='answer   answerof-413329 ' value='1601813'   \/><label for='answer-id-1601813' id='answer-label-1601813' class=' answer'><span>Inform the user of the expected RAG behavior<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413329[]' id='answer-id-1601814' class='answer   answerof-413329 ' value='1601814'   \/><label for='answer-id-1601814' id='answer-label-1601814' class=' answer'><span>Restrict access to the data sources to a limited number of users<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413329[]' id='answer-id-1601815' class='answer   answerof-413329 ' value='1601815'   \/><label for='answer-id-1601815' id='answer-label-1601815' class=' answer'><span>Curate upstream data properly that includes manual review before it is fed into the RAG system<\/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-413330'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from. <br \/>\r<br>Which will fulfill their need?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='413330' \/><input type='hidden' id='answerType413330' 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-413330[]' id='answer-id-1601816' class='answer   answerof-413330 ' value='1601816'   \/><label for='answer-id-1601816' id='answer-label-1601816' class=' answer'><span>context length 514; smallest model is 0.44GB and embedding dimension 768<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413330[]' id='answer-id-1601817' class='answer   answerof-413330 ' value='1601817'   \/><label for='answer-id-1601817' id='answer-label-1601817' class=' answer'><span>context length 2048: smallest model is 11GB and embedding dimension 2560<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413330[]' id='answer-id-1601818' class='answer   answerof-413330 ' value='1601818'   \/><label for='answer-id-1601818' id='answer-label-1601818' class=' answer'><span>context length 32768: smallest model is 14GB and embedding dimension 4096<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413330[]' id='answer-id-1601819' class='answer   answerof-413330 ' value='1601819'   \/><label for='answer-id-1601819' id='answer-label-1601819' class=' answer'><span>context length 512: smallest model is 0.13GB and embedding dimension 384<\/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-413331'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs. <br \/>\r<br>Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='413331' \/><input type='hidden' id='answerType413331' 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-413331[]' id='answer-id-1601820' class='answer   answerof-413331 ' value='1601820'   \/><label for='answer-id-1601820' id='answer-label-1601820' class=' answer'><span>Limit the number of relevant documents available for the RAG application to retrieve from<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413331[]' id='answer-id-1601821' class='answer   answerof-413331 ' value='1601821'   \/><label for='answer-id-1601821' id='answer-label-1601821' class=' answer'><span>Pick a smaller LLM that is domain-specific<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413331[]' id='answer-id-1601822' class='answer   answerof-413331 ' value='1601822'   \/><label for='answer-id-1601822' id='answer-label-1601822' class=' answer'><span>Limit the number of queries a customer can send per day<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413331[]' id='answer-id-1601823' class='answer   answerof-413331 ' value='1601823'   \/><label for='answer-id-1601823' id='answer-label-1601823' class=' answer'><span>Use the largest LLM possible because that gives the best performance for any general queries<\/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-413332'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A Generative Al Engineer is responsible for developing a chatbot to enable their company\u2019s internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration: <br \/>\r<br>call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives\u2019 call resolution from fields call_duration and call start_time. <br \/>\r<br>transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript <br \/>\r<br>as *.txt files. <br \/>\r<br>call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use. <br \/>\r<br>call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active. maintenance_schedule C a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes. <br \/>\r<br>They need sources that could add context to best identify ticket root cause and resolution. <br \/>\r<br>Which TWO sources do that? (Choose two.)<\/div><input type='hidden' name='question_id[]' id='qID_10' value='413332' \/><input type='hidden' id='answerType413332' 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-413332[]' id='answer-id-1601824' class='answer   answerof-413332 ' value='1601824'   \/><label for='answer-id-1601824' id='answer-label-1601824' class=' answer'><span>call_cust_history<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413332[]' id='answer-id-1601825' class='answer   answerof-413332 ' value='1601825'   \/><label for='answer-id-1601825' id='answer-label-1601825' class=' answer'><span>maintenance_schedule<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413332[]' id='answer-id-1601826' class='answer   answerof-413332 ' value='1601826'   \/><label for='answer-id-1601826' id='answer-label-1601826' class=' answer'><span>call_rep_history<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413332[]' id='answer-id-1601827' class='answer   answerof-413332 ' value='1601827'   \/><label for='answer-id-1601827' id='answer-label-1601827' class=' answer'><span>call_detail<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-413332[]' id='answer-id-1601828' class='answer   answerof-413332 ' value='1601828'   \/><label for='answer-id-1601828' id='answer-label-1601828' class=' answer'><span>transcript Volume<\/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-413333'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>What is the most suitable library for building a multi-step LLM-based workflow?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='413333' \/><input type='hidden' id='answerType413333' 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-413333[]' id='answer-id-1601829' class='answer   answerof-413333 ' value='1601829'   \/><label for='answer-id-1601829' id='answer-label-1601829' class=' answer'><span>Pandas<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413333[]' id='answer-id-1601830' class='answer   answerof-413333 ' value='1601830'   \/><label for='answer-id-1601830' id='answer-label-1601830' class=' answer'><span>TensorFlow<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413333[]' id='answer-id-1601831' class='answer   answerof-413333 ' value='1601831'   \/><label for='answer-id-1601831' id='answer-label-1601831' class=' answer'><span>PySpark<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413333[]' id='answer-id-1601832' class='answer   answerof-413333 ' value='1601832'   \/><label for='answer-id-1601832' id='answer-label-1601832' class=' answer'><span>LangChain<\/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-413334'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>When developing an LLM application, it\u2019s crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks. <br \/>\r<br>Which action is NOT appropriate to avoid legal risks?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='413334' \/><input type='hidden' id='answerType413334' 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-413334[]' id='answer-id-1601833' class='answer   answerof-413334 ' value='1601833'   \/><label for='answer-id-1601833' id='answer-label-1601833' class=' answer'><span>Reach out to the data curators directly before you have started using the trained model to let them know.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413334[]' id='answer-id-1601834' class='answer   answerof-413334 ' value='1601834'   \/><label for='answer-id-1601834' id='answer-label-1601834' class=' answer'><span>Use any available data you personally created which is completely original and you can decide what license to use.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413334[]' id='answer-id-1601835' class='answer   answerof-413334 ' value='1601835'   \/><label for='answer-id-1601835' id='answer-label-1601835' class=' answer'><span>Only use data explicitly labeled with an open license and ensure the license terms are followed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413334[]' id='answer-id-1601836' class='answer   answerof-413334 ' value='1601836'   \/><label for='answer-id-1601836' id='answer-label-1601836' class=' answer'><span>Reach out to the data curators directly after you have started using the trained model to let them know.<\/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-413335'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error. <br \/>\r<br><br><img decoding=\"async\" width=489 height=256 id=\"\u56fe\u7247 8\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/07\/image002-11.jpg\"><br><br \/>\r<br>Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain? <br \/>\r<br>A) <br \/>\r<br><br><img decoding=\"async\" width=459 height=174 id=\"\u56fe\u7247 7\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/07\/image003-11.jpg\"><br><br \/>\r<br>B) <br \/>\r<br><br><img decoding=\"async\" width=459 height=174 id=\"\u56fe\u7247 6\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/07\/image004-11.jpg\"><br><br \/>\r<br>C) <br \/>\r<br><br><img decoding=\"async\" width=459 height=194 src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/07\/image005-12.jpg\"><br><br \/>\r<br>D) <br \/>\r<br><br><img decoding=\"async\" width=459 height=173 id=\"\u56fe\u7247 5\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/07\/image006-11.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_13' value='413335' \/><input type='hidden' id='answerType413335' 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-413335[]' id='answer-id-1601837' class='answer   answerof-413335 ' value='1601837'   \/><label for='answer-id-1601837' id='answer-label-1601837' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413335[]' id='answer-id-1601838' class='answer   answerof-413335 ' value='1601838'   \/><label for='answer-id-1601838' id='answer-label-1601838' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413335[]' id='answer-id-1601839' class='answer   answerof-413335 ' value='1601839'   \/><label for='answer-id-1601839' id='answer-label-1601839' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413335[]' id='answer-id-1601840' class='answer   answerof-413335 ' value='1601840'   \/><label for='answer-id-1601840' id='answer-label-1601840' class=' answer'><span>Option D<\/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-413336'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable. <br \/>\r<br>Which change could the Generative Al Engineer perform to mitigate this issue?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='413336' \/><input type='hidden' id='answerType413336' 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-413336[]' id='answer-id-1601841' class='answer   answerof-413336 ' value='1601841'   \/><label for='answer-id-1601841' id='answer-label-1601841' class=' answer'><span>Split the LLM output by newline characters to truncate away the summarization explanation.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413336[]' id='answer-id-1601842' class='answer   answerof-413336 ' value='1601842'   \/><label for='answer-id-1601842' id='answer-label-1601842' class=' answer'><span>Tune the chunk size of news articles or experiment with different embedding models.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413336[]' id='answer-id-1601843' class='answer   answerof-413336 ' value='1601843'   \/><label for='answer-id-1601843' id='answer-label-1601843' class=' answer'><span>Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413336[]' id='answer-id-1601844' class='answer   answerof-413336 ' value='1601844'   \/><label for='answer-id-1601844' id='answer-label-1601844' class=' answer'><span>Provide few shot examples of desired output format to the system and\/or user prompt.<\/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-413337'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>A Generative Al Engineer has developed an LLM application to answer questions about internal <br \/>\r<br>company policies. The Generative AI Engineer must ensure that the application doesn\u2019t hallucinate or leak confidential data. <br \/>\r<br>Which approach should NOT be used to mitigate hallucination or confidential data leakage?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='413337' \/><input type='hidden' id='answerType413337' 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-413337[]' id='answer-id-1601845' class='answer   answerof-413337 ' value='1601845'   \/><label for='answer-id-1601845' id='answer-label-1601845' class=' answer'><span>Add guardrails to filter outputs from the LLM before it is shown to the user<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413337[]' id='answer-id-1601846' class='answer   answerof-413337 ' value='1601846'   \/><label for='answer-id-1601846' id='answer-label-1601846' class=' answer'><span>Fine-tune the model on your data, hoping it will learn what is appropriate and not<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413337[]' id='answer-id-1601847' class='answer   answerof-413337 ' value='1601847'   \/><label for='answer-id-1601847' id='answer-label-1601847' class=' answer'><span>Limit the data available based on the user\u2019s access level<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413337[]' id='answer-id-1601848' class='answer   answerof-413337 ' value='1601848'   \/><label for='answer-id-1601848' id='answer-label-1601848' class=' answer'><span>Use a strong system prompt to ensure the model aligns with your needs.<\/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-413338'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>A Generative Al Engineer interfaces with an LLM with prompt\/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output \u201cIn Stock\u201d if the product is available or only the term \u201cOut of Stock\u201d if not. <br \/>\r<br>Which prompt will work to allow the engineer to respond to call classification labels correctly?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='413338' \/><input type='hidden' id='answerType413338' 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-413338[]' id='answer-id-1601849' class='answer   answerof-413338 ' value='1601849'   \/><label for='answer-id-1601849' id='answer-label-1601849' class=' answer'><span>Respond with \u201cIn Stock\u201d if the customer asks for a product.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413338[]' id='answer-id-1601850' class='answer   answerof-413338 ' value='1601850'   \/><label for='answer-id-1601850' id='answer-label-1601850' class=' answer'><span>You will be given a customer call transcript where the customer asks about product availability. The outputs are either \u201cIn Stock\u201d or \u201cOut of Stock\u201d. Format the output in JSON, for example: {\u201ccall_id\u201d: \u201c123\u201d, \u201clabel\u201d: \u201cIn Stock\u201d}.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413338[]' id='answer-id-1601851' class='answer   answerof-413338 ' value='1601851'   \/><label for='answer-id-1601851' id='answer-label-1601851' class=' answer'><span>Respond with \u201cOut of Stock\u201d if the customer asks for a product.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413338[]' id='answer-id-1601852' class='answer   answerof-413338 ' value='1601852'   \/><label for='answer-id-1601852' id='answer-label-1601852' class=' answer'><span>You will be given a customer call transcript where the customer inquires about product availability. \r\nRespond with \u201cIn Stock\u201d if the product is available or \u201cOut of Stock\u201d if not.<\/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-413339'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they\u2019re willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties. <br \/>\r<br>Which model meets all the Generative Al Engineer\u2019s needs in this situation?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='413339' \/><input type='hidden' id='answerType413339' 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-413339[]' id='answer-id-1601853' class='answer   answerof-413339 ' value='1601853'   \/><label for='answer-id-1601853' id='answer-label-1601853' class=' answer'><span>Dolly 1.5B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413339[]' id='answer-id-1601854' class='answer   answerof-413339 ' value='1601854'   \/><label for='answer-id-1601854' id='answer-label-1601854' class=' answer'><span>OpenAI GPT-4<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413339[]' id='answer-id-1601855' class='answer   answerof-413339 ' value='1601855'   \/><label for='answer-id-1601855' id='answer-label-1601855' class=' answer'><span>BGE-large<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413339[]' id='answer-id-1601856' class='answer   answerof-413339 ' value='1601856'   \/><label for='answer-id-1601856' id='answer-label-1601856' class=' answer'><span>Llama2-70B<\/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-413340'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A Generative Al Engineer would like an LLM to generate formatted JSON from emails. <br \/>\r<br>This will require parsing and extracting the following information: order ID, date, and sender email. <br \/>\r<br>Here\u2019s a sample email:<br \/>\r<br><br><img decoding=\"async\" width=650 height=208 id=\"\u56fe\u7247 4\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/07\/image007-12.jpg\"><br><br \/>\r<br>They will need to write a prompt that will extract the relevant information in JSON format with the <br \/>\r<br>highest level of output accuracy. <br \/>\r<br>Which prompt will do that?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='413340' \/><input type='hidden' id='answerType413340' 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-413340[]' id='answer-id-1601857' class='answer   answerof-413340 ' value='1601857'   \/><label for='answer-id-1601857' id='answer-label-1601857' class=' answer'><span>You will receive customer emails and need to extract date, sender email, and order I<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601858' class='answer   answerof-413340 ' value='1601858'   \/><label for='answer-id-1601858' id='answer-label-1601858' class=' answer'><span>You should return the date, sender email, and order ID information in JSON format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601859' class='answer   answerof-413340 ' value='1601859'   \/><label for='answer-id-1601859' id='answer-label-1601859' class=' answer'><span>You will receive customer emails and need to extract date, sender email, and order I<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601860' class='answer   answerof-413340 ' value='1601860'   \/><label for='answer-id-1601860' id='answer-label-1601860' class=' answer'><span>Return the extracted information in JSON format. \r\nHere\u2019s an example: {\u201cdate\u201d: \u201cApril 16, 2024\u201d, \u201csender_email\u201d: \u201csarah.lee925@gmail.com\u201d, \u201corder_id\u201d: \u201cRE987D\u201d}<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601861' class='answer   answerof-413340 ' value='1601861'   \/><label for='answer-id-1601861' id='answer-label-1601861' class=' answer'><span>You will receive customer emails and need to extract date, sender email, and order I<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601862' class='answer   answerof-413340 ' value='1601862'   \/><label for='answer-id-1601862' id='answer-label-1601862' class=' answer'><span>Return the extracted information in a human-readable format.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601863' class='answer   answerof-413340 ' value='1601863'   \/><label for='answer-id-1601863' id='answer-label-1601863' class=' answer'><span>You will receive customer emails and need to extract date, sender email, and order I<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413340[]' id='answer-id-1601864' class='answer   answerof-413340 ' value='1601864'   \/><label for='answer-id-1601864' id='answer-label-1601864' class=' answer'><span>Return the extracted information in JSON format.<\/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-413341'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible. <br \/>\r<br>Which combination of chaining components and configuration meets these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='413341' \/><input type='hidden' id='answerType413341' 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-413341[]' id='answer-id-1601865' class='answer   answerof-413341 ' value='1601865'   \/><label for='answer-id-1601865' id='answer-label-1601865' class=' answer'><span>For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413341[]' id='answer-id-1601866' class='answer   answerof-413341 ' value='1601866'   \/><label for='answer-id-1601866' id='answer-label-1601866' class=' answer'><span>The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413341[]' id='answer-id-1601867' class='answer   answerof-413341 ' value='1601867'   \/><label for='answer-id-1601867' id='answer-label-1601867' class=' answer'><span>For the question-answering application, prompt engineering and an LLM are required to generate answers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413341[]' id='answer-id-1601868' class='answer   answerof-413341 ' value='1601868'   \/><label for='answer-id-1601868' id='answer-label-1601868' class=' answer'><span>For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.<\/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-413342'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team\u2019s latest standings. <br \/>\r<br>How could the Generative AI Engineer best design these capabilities into their system?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='413342' \/><input type='hidden' id='answerType413342' 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-413342[]' id='answer-id-1601869' class='answer   answerof-413342 ' value='1601869'   \/><label for='answer-id-1601869' id='answer-label-1601869' class=' answer'><span>Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413342[]' id='answer-id-1601870' class='answer   answerof-413342 ' value='1601870'   \/><label for='answer-id-1601870' id='answer-label-1601870' class=' answer'><span>Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413342[]' id='answer-id-1601871' class='answer   answerof-413342 ' value='1601871'   \/><label for='answer-id-1601871' id='answer-label-1601871' class=' answer'><span>Instruct the LLM to respond with \u201cRAG\u201d, \u201cAPI\u201d, or \u201cTABLE\u201d depending on the query, then use text parsing and conditional statements to resolve the query.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-413342[]' id='answer-id-1601872' class='answer   answerof-413342 ' value='1601872'   \/><label for='answer-id-1601872' id='answer-label-1601872' class=' answer'><span>Build a system prompt with all possible event dates and table information in the system prompt. 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