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Databricks Generative AI Engineer Associate Exam Practice Questions, practice question pack
DATABRICKS · Generative AI Engineer Associate

Databricks Generative AI Engineer Associate Exam Practice Questions

99% pass rateUpdated 1 Nov 2026123 questions2 PDFs by email
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(1 customer review)

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Every question with the correct answer and why the other options are wrong.

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99% pass rate. Pass or your money back.Fail the exam after using this pack and we refund it, or swap it for any other pack. How the guarantee works
The Databricks-Generative-AI-Engineer-Associate file didn’t feel too heavy or complicated. Each topic was short but meaningful, which helped me stay focused through the week without losing motivation halfway.
Wilder JamesGenerative AI Engineer Associate
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Three of the 123 questions in this pack

Question 1

A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible. Which combination of chaining components and configuration meets these requirements?

  1. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.
  2. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
  3. For the question-answering application, prompt engineering and an LLM are required to generate answers.
  4. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
Show answer and explanation

Correct answer: A. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.

Option A describes the core RAG (Retrieval-Augmented Generation) pattern: a retriever component fetches relevant documents, which are then inserted into a prompt for the LLM. This approach requires minimal development effort, achieves low operational cost by avoiding fine-tuning, and handles frequently updated documents naturally since the retriever simply fetches current content from the document store. The combination of prompt, retriever, and LLM is the industry-standard solution for this use case.

Why the other options are wrong

  • B. Fine-tuning an LLM with new documents is expensive, requires retraining cycles, and doesn't scale well with frequent updates.
  • C. Prompt engineering alone cannot access new documents; the system has no mechanism to incorporate external knowledge sources.
  • D. Fine-tuning a custom LLM is costly in both development and operational expenses and is unnecessary when a retriever can dynamically fetch relevant content.

Question 2

A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team’s latest standings. How could the Generative AI Engineer best design these capabilities into their system?

  1. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
  2. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
  3. Instruct the LLM to respond with “RAG”, “API”, or “TABLE” depending on the query, then use text parsing and conditional statements to resolve the query.
  4. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.
Show answer and explanation

Correct answer: B. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.

Option B describes an agent-based system where the LLM decides which tools to use based on a system prompt listing available capabilities (text search, API calls, table queries). This is the proper agent design pattern that allows the system to route different query types to different tools, the LLM acts as a reasoning engine to select appropriate actions. The system prompt declares the tools, and the agent orchestrates their use across multiple calls to solve complex queries.

Why the other options are wrong

  • A. RAG alone cannot handle API calls or table queries; it only retrieves text from vector stores and is insufficient for multi-tool scenarios.
  • C. Explicit text parsing and conditional logic creates brittle, hard-coded decision trees that don't leverage the LLM's reasoning capabilities and don't scale well.
  • D. Embedding all event dates and table data in the system prompt violates best practices, wastes context window tokens, creates maintenance overhead, and doesn't use tools appropriately.

Question 3

A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation. Which set of high level tasks should the Generative AI Engineer's system perform?

  1. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
  2. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
  3. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
  4. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
Show answer and explanation

Correct answer: D. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.

Option D describes the standard RAG pipeline for document-based QA: split documents into chunks, embed them into a vector store, retrieve relevant chunks using semantic similarity to the user query, and pass the retrieved context to the LLM for answer generation. This approach is scalable, maintainable, and effective for HR documentation scenarios where precise retrieval and grounding in source documents is essential.

Why the other options are wrong

  • A. While document-level retrieval can work, averaging embeddings at the document level loses granularity and relevance compared to chunk-level retrieval, reducing answer quality.
  • B. Pre-summarizing documents creates information loss and reduces the ability to ground answers in original source material; summaries are less precise than original chunks.
  • C. Using ALS matrix factorization on historical questions adds unnecessary complexity, requires substantial historical data that may not exist, and doesn't adapt well to new question patterns compared to semantic embedding methods.

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REVIEWS

1 review for Databricks Generative AI Engineer Associate Exam Practice Questions

  1. Wilder James –

    The Databricks-Generative-AI-Engineer-Associate file didn’t feel too heavy or complicated. Each topic was short but meaningful, which helped me stay focused through the week without losing motivation halfway.

Only logged in customers who have purchased this product may leave a review.

How much of the Databricks Generative AI Engineer Associate bank is free?

123 questions in the bank

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ABOUT THIS PACK

What’s in the Databricks Generative AI Engineer Associate pack

  • 123 questions mapped to the Generative AI Engineer Associate exam guide
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Delivered by email shortly after checkout
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A Generative AI Engineer Associate attempt costs US$200. This pack is US$39, paid once.

What makes the Generative AI Engineer Associate exam hard

Generative AI Engineer Associate is the fastest growing Databricks certification and the most scenario driven of them: 45 scored questions in 90 minutes, each describing a use case with a constraint, a latency budget, a data sensitivity or a cost ceiling, and four options that would all more or less run.

It is a decision-making exam about building RAG and agent applications on Databricks, with all machine learning code in Python (other data manipulation code may be SQL), and the March 2026 edition of the guide reflects Mosaic AI Agent Framework, Vector Search and Model Serving as they stand now.

Application Development is the largest section at 30%: building RAG chains and agents with LangChain and the Mosaic AI Agent Framework, prompt engineering and guardrails, tool calling, retrievers and rerankers, chunking strategies and embedding model selection. Assembling and Deploying Applications at 22% covers packaging chains with MLflow, Unity Catalog registration, Model Serving endpoints and Vector Search index creation. The remaining sections cover design, data preparation, evaluation and monitoring, and governance, and the skill being tested is spotting the option that satisfies the constraint, not just the one that is technically sound.

About the exam

The Databricks Certified Generative AI Engineer Associate validates the ability to design, build, deploy, govern and evaluate generative AI applications such as RAG pipelines and agents on the Databricks Data Intelligence Platform. There are no prerequisites; Databricks recommends related training and six months of hands-on experience.

Questions before you buy

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99% of customers who prepare with a CertStash pack pass. If you do not, we refund the pack, or swap it for another pack.

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