GENAI-ASSOCIATE practice questions 1–10

1. Question 1 · 92 in bank

   A Generative AI Engineer is building a RAG application for answering employee questions on company policies. What are the steps needed to build this RAG application and deploy it?

   • AIngest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving
   • BUser submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
   • CIngest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -> Deploy it using Model Serving -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response
   • DIngest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving

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   Answer: D

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2. Question 2 · 92 in bank

   Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product. What can the engineer do to improve the relevance of the RAG’s response?

   • AAssess the quality of the retrieved context
   • BImplement caching for frequently asked questions
   • CUse a different LLM to improve the generated response
   • DUse a different semantic similarity search algorithm

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   Answer: D

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3. Question 3 · 92 in bank

   A Generative AI Engineer at a legal firm is designing a RAG system to analyze historical legal case precedents. The system needs to process millions of court opinions and legal documents, already organized by time and topic, to track how interpretations of specific laws have evolved over time. All of these documents are in plain-text. The engineer needs to choose a chunking method that would most effectively preserve continuity and the temporal nature of the cases. Which method do they choose?

   • AImplement windowed summarization with overlapping chunks.
   • BImplement a hierarchical tree structure, like RAPTOR, to group similar legal concepts.
   • CImplement paragraph level embeddings with each chunk.
   • DImplement sentence level embeddings with each chunk tagged with the time to enable metadata filtering.

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   Answer: A

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4. Question 4 · 92 in bank

   A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot’s focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message: “Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.” Which framework type should be implemented to solve this?

   • ASafety Guardrail
   • BSecurity Guardrail
   • CContextual Guardrail
   • DCompliance Guardrail

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   Answer: C

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5. Question 5 · 92 in bank

   A Generative AI Engineer is developing an agent system using a popular agent-authoring library. The agent comprises multiple parallel and sequential chains. The engineer encounters challenges as the agent fails at one of the steps, making it difficult to debug the root cause. They need to find an appropriate approach to research this issue and discover the cause of failure. Which approach do they choose?

   • AEnable MLflow tracing to gain visibility into each agent's behavior and execution step.
   • BRun MLflow.evaluate to determine root cause of failed step.
   • CImplement structured logging within the agent's code to capture detailed execution information.
   • DDeconstruct the agent into independent steps to simplify debugging.

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   Answer: A

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6. Question 6 · 92 in bank

   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. How should the Generative Al Engineer architect their system?

   • ACreate 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.
   • BCreate 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’ profiles and perform keyword matching to find the best available team member.
   • CCreate 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.
   • DCreate 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.

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   Answer: D

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7. Question 7 · 92 in bank

   A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient’s question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor’s office and suggest a few relevant pre-approved medical articles for reading. If the patient’s question is urgent, direct the patient to calling their local emergency services. Given the following user input: “I have been experiencing severe headaches and dizziness for the past two days.” Which response is most appropriate for the chatbot to generate?

   • AHere are a few relevant articles for your browsing. Let me know if you have questions after reading them.
   • BPlease call your local emergency services.
   • CHeadaches can be tough. Hope you feel better soon!
   • DPlease provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness.

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   Answer: B

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8. Question 8 · 92 in bank

   A Generative AI Engineer is experimenting with using parameters to configure an agent in Mosaic Agent Framework. However, they are struggling to get the agent to respond with relevant information with this configuration:

   

   Which error is causing the problem?

   • AThe prompt does not parse the user's input vars
   • BThe prompt does not set the retriever schema
   • CThe prompt does not list available agents for the LLM to call
   • DThe prompt is not wrapped in ChatModel

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   Answer: A

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9. Question 9 · 92 in bank

   A Generative AI Engineer is building an LLM to generate article headlines given the article content. However, the initial output from the LLM does not match the desired tone or style. Which approach would be most effective for adjusting the LLM’s response to achieve the desired response?

   • AExclude any article headlines that do not match the desired output
   • BFine-tune the LLM on a dataset of desired tone and style
   • CProvide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
   • DAll of the above

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   Answer: C

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10. Question 10 · 92 in bank

    Select 2

    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’s 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’s 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. Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

    • AChange embedding models and compare performance.
    • BAdd a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.
    • CChoose 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.
    • DPass 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.
    • ECreate 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.

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    Answer: CE

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GENAI-ASSOCIATE practice, question 1. 92 questions available.