GENERATIVE-AI-ENGINEER-ASSOCIATE — questions

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GENERATIVE-AI-ENGINEER-ASSOCIATE Question 82

Topic 1 · Question 82

A Generative AI Engineer has been reviewing issues with their company's LLM based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?

AIt allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses. (correct answer)
BIt allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated.
CIt allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases.
DIt reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens.

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 83

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Topic 1 · Question 83 · Select all that apply

An AI developer team wants to fine tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs, and are using Huggingface model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection? (Choose two.)

ABig Code Models Leaderboard (correct answer)
BNumber of model parameters (correct answer)
CMTEB Leaderboard
DChatbot Arena Leaderboard
ENumber of model downloads last month

Reveal answer & explanation

Correct answer: A, B

The correct answer is A, B. Option A: Big Code Models Leaderboard Option B: Number of model parameters

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 84

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Topic 1 · Question 84

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 (correct answer)
BImplementing metadata filtering based on car models and years
CFine-tuning an embedding model on automotive terminology
DAdding few-shot examples for response generation

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Setting chunk size to match the model's context window to maximize coverage

Explanation

Retrieval-augmented generation grounds model responses in retrieved enterprise data to improve relevance and reduce unsupported claims.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 85

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Topic 1 · Question 85

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. (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Implement sentence level embeddings with each chunk tagged with the time to enable metadata filtering.

Explanation

An embedding maps content into a numeric vector so semantically similar items can be retrieved by distance.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 86

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Topic 1 · Question 86

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. (correct answer)
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.

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Enable MLflow tracing to gain visibility into each agent's behavior and execution step.

Explanation

MLflow tracks experiments, packages models, manages model versions, and supports governed deployment workflows.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 87

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Topic 1 · Question 87

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 (correct answer)
CThe prompt does not list available agents for the LLM to call
DThe prompt is not wrapped in ChatModel

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: The prompt does not set the retriever schema

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 88

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Topic 1 · Question 88

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 (correct answer)
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

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 89

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Topic 1 · Question 89

All of the following are python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?

AOpenAI client
BREST API via requests library (correct answer)
CMLflow Deployments SDK
DDatabricks Python SDK

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: REST API via requests library

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 90

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Topic 1 · Question 90

A Generative AI Engineer is deploying a customer-facing, fine-tuned LLM on their public website. Given the large investment the company put into fine tuning this model, and the proprietary nature of the tuning data, they are concerned about model inversion attacks. Which of the following Databricks AI Security Framework (DASF) risk mitigation strategies are most relevant to this use case?

AImplement AI guardrails to allow users to configure and enforce compliance
BLeverage Databricks access control lists (ACLs) to configure permissions for accessing models
CUse secure model features with Databricks Feature Store
DApply attribute-based access controls (ABAC) to limit unauthorized access (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Apply attribute-based access controls (ABAC) to limit unauthorized access This option keeps traffic private / properly secured as required.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 91

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Topic 1 · Question 91

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. (correct answer)
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.

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Use a reranker to order the documents based on the relevance scores.

GENERATIVE-AI-ENGINEER-ASSOCIATE Question 92

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Topic 1 · Question 92

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 (correct answer)
BQuality metrics like correctness and guideline adherence
CBoth operational and quality metrics
DNo metrics are automatically captured

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Operational metrics like request volume, latency, and errors This option meets the real-time / low-latency performance requirement.

Showing questions 81–91 of 91 · Page 5 of 5