Download Certified Generative AI Engineer Associate.CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE.CertDumps.2026-01-21.89q.vcex

Vendor: Databricks
Exam Code: CERTIFIED-GENERATIVE-AI-ENGINEER-ASSOCIATE
Exam Name: Certified Generative AI Engineer Associate
Date: Jan 21, 2026
File Size: 274 KB
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Demo Questions

Question 1
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?
  1. Provide an LLM argument to PromptTemplate()
  2. Provide template and LLM arguments to PromptTemplate()
  3. Omit PromptTemplate(), it is only used for multi-part templates
  4. Provide a template argument to PromptTemplate()
Correct answer: D
Question 2
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?
  1. Enable MLflow tracing to gain visibility into each agent's behavior and execution step.
  2. Run MLflow.evaluate to determine root cause of failed step.
  3. Implement structured logging within the agent's code to capture detailed execution information.
  4. Deconstruct the agent into independent steps to simplify debugging.
Correct answer: C
Question 3
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?
  1. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent
  2. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
  3. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
  4. 1. 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
Correct answer: B
Question 4
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?
  1. Use a reranker to order the documents based on the relevance scores.
  2. Modify the prompt to instruct the LLM to order the documents based on the relevance scores.
  3. Use a different embedding model for computing document embeddings.
  4. Increase the number of documents added to the prompt to improve context relevance.
Correct answer: A
Question 5
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?
  1. Implement windowed summarization with overlapping chunks.
  2. Implement a hierarchical tree structure, like RAPTOR, to group similar legal concepts.
  3. Implement paragraph level embeddings with each chunk.
  4. Implement sentence level embeddings with each chunk tagged with the time to enable metadata filtering.
Correct answer: A
Question 6
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?
  1. Setting chunk size to match the model's context window to maximize coverage
  2. Implementing metadata filtering based on car models and years
  3. Fine-tuning an embedding model on automotive terminology
  4. Adding few-shot examples for response generation
Correct answer: C
Question 7
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?
  1. Implement AI guardrails to allow users to configure and enforce compliance
  2. Leverage Databricks access control lists (ACLs) to configure permissions for accessing models
  3. Use secure model features with Databricks Feature Store
  4. Apply attribute-based access controls (ABAC) to limit unauthorized access
Correct answer: B
Question 8
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?
  1. Operational metrics like request volume, latency, and errors
  2. Quality metrics like correctness and guideline adherence
  3. Both operational and quality metrics
  4. No metrics are automatically captured
Correct answer: A
Question 9
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?
  1. OpenAI client
  2. REST API via requests library
  3. MLflow Deployments SDK
  4. Databricks Python SDK
Correct answer: B
Question 10
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?
  1. pytesseract
  2. numpy
  3. pypdf2
  4. beautifulsoup
Correct answer: D
Question 11
A Generative AI Engineer who was prototyping an LLM system accidentally ran thousands of inference queries against a Foundation Model endpoint over the weekend. They want to take action to prevent this from unintentionally happening again in the future.
What action should they take?
  1. Use prompt engineering to instruct the LLM endpoints to refuse too many subsequent queries.
  2. Require that all development code which interfaces with a Foundation Model endpoint must be reviewed by a Staff level engineer before execution.
  3. Build a pyfunc model which proxies to the Foundation Model endpoint and add throttling within the pyfune model.
  4. Configure rate limiting on the Foundation Model endpoints.
Correct answer: D
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