Databricks Databricks-Generative-AI-Engineer-Associate real exam prep : Databricks Certified Generative AI Engineer Associate

  • Exam Code: Databricks-Generative-AI-Engineer-Associate
  • Exam Name: Databricks Certified Generative AI Engineer Associate
  • Updated: Oct 10, 2026
  • Q&As: 94 Questions and Answers

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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain
  • similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
Topic 2
  • Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 3
  • Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.
Topic 4
  • Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.

Reference: https://www.databricks.com/learn/certification/genai-engineer-associate

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Databricks Certified Generative AI Engineer Associate Sample Questions:

Question #1

A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

  • A. CodeLlama-34B
  • B. MPT-7b
  • C. BGE-large
  • D. Llama2-70b
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

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?

  • A. Input: Online chat logs; Output: Buttons that represent choices for booking details
  • B. Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions
  • C. Input: Customer reviews; Output: Classify review sentiment
  • D. Input: Online chat logs; Output: Cancellation options
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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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?

  • A. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
  • B. 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
  • C. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent
  • D. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM ' s on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

  • A. BLEU metric
  • B. NDCG metric
  • C. RECALL metric
  • D. ROUGE metric
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

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?

  • A. Use the largest LLM possible because that gives the best performance for any general queries
  • B. Pick a smaller LLM that is domain-specific
  • C. Limit the number of relevant documents available for the RAG application to retrieve from
  • D. Limit the number of queries a customer can send per day
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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