GENAI-ASSOCIATE practice questions 31–40

1. Question 31 · 92 in bank

   A Generative AI Engineer is using LangChain to assist a museum in classifying documents and using this code:

   

   Their code results in an error. What do they need to change in order to fix this template?

   • AProvide an LLM argument to PromptTemplate()
   • BProvide template and LLM arguments to PromptTemplate()
   • COmit PromptTemplate(), it is only used for multi-part templates
   • DProvide a template argument to PromptTemplate()

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   Answer: D

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2. Question 32 · 92 in bank

   A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have: • A catalog with hundreds of thousands of cars manufactured since the 1960s • Historical searches, with user queries and successful matches • Descriptions of their own cars in multiple languages They have already selected an open source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?

   • ASetting chunk size to match the model's context window to maximize coverage
   • BImplementing metadata filtering based on car models and years
   • CFine-tuning an embedding model on automotive terminology
   • DAdding few-shot examples for response generation

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   Answer: A

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3. Question 33 · 92 in bank

   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 “In Stock” if the product is available or only the term “Out of Stock” if not. Which prompt will work to allow the engineer to respond to call classification labels correctly?

   • ARespond with “In Stock” if the customer asks for a product.
   • BYou will be given a customer call transcript where the customer asks about product availability. The outputs are either “In Stock” or “Out of Stock”. Format the output in JSON, for example: {“call_id”: “123”, “label”: “In Stock”}.
   • CRespond with “Out of Stock” if the customer asks for a product.
   • DYou will be given a customer call transcript where the customer inquires about product availability. Respond with “In Stock” if the product is available or “Out of Stock” if not.

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   Answer: D

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4. Question 34 · 92 in bank

   A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs. Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

   • ALimit the number of relevant documents available for the RAG application to retrieve from
   • BPick a smaller LLM that is domain-specific
   • CLimit the number of queries a customer can send per day
   • DUse the largest LLM possible because that gives the best performance for any general queries

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   Answer: B

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5. Question 35 · 92 in bank

   A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this?

   • A1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent
   • B1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
   • C1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
   • D1. Define the tools inside the agents 2. Load the agents into the LLM 3. Instruct the LLM to use CoT reasoning to determine the appropriate agent

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   Answer: B

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6. Question 36 · 92 in bank

   A generative AI engineer is deploying an AI agent authored with MLflow's ChatAgent interface for a retail company's customer support system on Databricks. The agent must handle thousands of inquiries daily, and the engineer needs to track its performance and quality in real-time to ensure it meets service-level agreements. Which metrics are automatically captured by default and made available for monitoring when the agent is deployed using the Mosaic AI Agent Framework?

   • AOperational metrics like request volume, latency, and errors
   • BQuality metrics like correctness and guideline adherence
   • CBoth operational and quality metrics
   • DNo metrics are automatically captured

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   Answer: A

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7. Question 37 · 92 in bank

   A Generative AI Engineer is building a system that will answer questions on currently unfolding news topics. As such, it pulls information from a variety of sources including articles and social media posts. They are concerned about toxic posts on social media causing toxic outputs from their system. Which guardrail will limit toxic outputs?

   • AReduce the amount of context items the system will include in consideration for its response.
   • BUse only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM.
   • CLog all LLM system responses and perform a batch toxicity analysis monthly.
   • DImplement rate limiting.

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   Answer: B

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8. Question 38 · 92 in bank

   A Generative AI Engineer is developing an LLM application to interact with users to provide personalized movie recommendations. Given the potential for malicious user inputs, which technique would be most effective in safeguarding the application?

   • AReduce the time that the users can interact with the LLM
   • BIncrease the amount of compute that powers the LLM to process input faster
   • CAsk the LLM to remind the user that the input is malicious but continue the conversation with the user
   • DImplement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

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   Answer: D

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9. Question 39 · 92 in bank

   A Generative AI Engineer developed an LLM application using the pay-per-token Foundation Model API. Now that the application is ready to be deployed, they would like to ensure the model endpoint can serve high incoming volumes of requests in production. What should the Generative AI Engineer consider?

   • ASwitch to using External Models instead
   • BThrottle the incoming batch of requests manually to avoid rate limiting issues
   • CChange to a model with a fewer number of parameters in order to reduce hardware constraint issues
   • DDeploy the model using provisioned throughput as it comes with performance guarantees

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   Answer: D

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10. Question 40 · 92 in bank

    A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in HTML format. They want to develop a solution using the least amount of lines of code. Which Python package should be used to extract the text from the source documents?

    • Apytesseract
    • Bnumpy
    • Cpypdf2
    • Dbeautifulsoup

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    Answer: D

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GENAI-ASSOCIATE practice, question 31. 92 questions available.