GENAI-ASSOCIATE practice questions 21–30

1. Question 21 · 92 in bank

   A Generative AI Engineer is asked to build an LLM application that would excel at code generation. They need to select a model that has been specifically trained to generate code. Which model would likely produce the best results out of the box?

   • ACodeLlama-34b-Instruct-hf
   • BMixtral-8x7B-v0.1
   • CLlama-2-70b-hf
   • Dmpt-7b-8k-instruct

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   Answer: A

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2. Question 22 · 92 in bank

   A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform. Which input/output pair will support their goal?

   • AInput: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions
   • BInput: Online chat logs; Output: Buttons that represent choices for booking details
   • CInput: Customer reviews; Output: Classify review sentiment
   • DInput: Online chat logs; Output: Cancellation options

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   Answer: B

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3. Question 23 · 92 in bank

   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. Which change could the Generative Al Engineer perform to mitigate this issue?

   • ASplit the LLM output by newline characters to truncate away the summarization explanation.
   • BTune the chunk size of news articles or experiment with different embedding models.
   • CRevisit their document ingestion logic, ensuring that the news articles are being ingested properly.
   • DProvide few shot examples of desired output format to the system and/or user prompt.

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   Answer: D

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4. Question 24 · 92 in bank

   A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn’t hallucinate or leak confidential data. Which approach should NOT be used to mitigate hallucination or confidential data leakage?

   • AAdd guardrails to filter outputs from the LLM before it is shown to the user
   • BFine-tune the model on your data, hoping it will learn what is appropriate and not
   • CLimit the data available based on the user’s access level
   • DUse a strong system prompt to ensure the model aligns with your needs.

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   Answer: B

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5. Question 25 · 92 in bank

   A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries. Which metric should they monitor for their customer service LLM application in production?

   • ANumber of customer inquiries processed per unit of time
   • BEnergy usage per query
   • CFinal perplexity scores for the training of the model
   • DHuggingFace Leaderboard values for the base LLM

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   Answer: A

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6. Question 26 · 92 in bank

   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. Which will fulfill their need?

   • Acontext length 514; smallest model is 0.44GB and embedding dimension 768
   • Bcontext length 2048: smallest model is 11GB and embedding dimension 2560
   • Ccontext length 32768: smallest model is 14GB and embedding dimension 4096
   • Dcontext length 512: smallest model is 0.13GB and embedding dimension 384

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   Answer: D

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7. Question 27 · 92 in bank

   A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport. 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
   • BIngest 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
   • CIngest documents from a source –> Index the documents and save to Vector Search –> Evaluate model –> Deploy it using Model Serving
   • DUser 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

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   Answer: B

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8. Question 28 · 92 in bank

   Generative AI Engineer is helping a cinema extend its website’s chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability in their RAG application. Which option will do this with the least effort and in the most performant way?

   • ACreate a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation.
   • BQuery the Delta table directly via a SQL query constructed from the user’s input using a text-to-SQL LLM in the agent logic / tool implementation.
   • CSet up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation.
   • DWrite the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation.

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   Answer: A

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9. Question 29 · 92 in bank

   A Generative AI Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered AI solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this. Which input/output pair will do this?

   • AInput: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user’s interactions, then respond
   • BInput: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary
   • CInput: Customer reviews; Output: Classify review sentiment
   • DInput: Customer reviews; Output: Group the reviews by users and aggregate per-user average rating, then respond

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   Answer: A

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10. Question 30 · 92 in bank

    A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher-ranked documents. Which option should the team consider to optimize this workflow?

    • AUse a reranker to order the documents based on the relevance scores.
    • BModify the prompt to instruct the LLM to order the documents based on the relevance scores.
    • CUse a different embedding model for computing document embeddings.
    • DIncrease the number of documents added to the prompt to improve context relevance.

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    Answer: A

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GENAI-ASSOCIATE practice, question 21. 92 questions available.