Free Databricks Databricks-Generative-AI-Engineer-Associate Practice Test & Real Exam Questions

Exam Code/Number: Databricks-Generative-AI-Engineer-Associate
Exam Name/Title: Databricks Certified Generative AI Engineer Associate
Certification Provider: Databricks
Corresponding Certification: Generative AI Engineer

Exam Questions: 75
Updated On: Jun 27, 2026

Question #9

A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?

A. Vector database

B. Response-generating LLM

C. Embedding model

D. Invite users to submit long, rather than concise, questions

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #10

A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

A. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn

B. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

C. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

D. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #11

A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

A. The chatbot should only look at previous event information

B. The chatbot should only process payments

C. There should be two different chatbots handling different types of user queries.

D. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #12

A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be "Tell me about monster truck news around January 5th 1992". They want to do this with the least amount of effort.
How can they set up their Vector Search index to support this use case?

A. Include metadata columns for article date and topic to support metadata filtering.

B. pass the query directly to the vector search index and return the best articles.

C. Create separate indexes by topic and add a classifier model to appropriately pick the best index.

D. Split articles by 10 day blocks and return the block closest to the query.

Discussion 0

Correct Answer: A Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #13

A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?

A. Compare the Levenshtein distances of returned results against a representative sample of test inputs

B. Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs

C. Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs

D. Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #14

Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

A. External tools

B. Conversation Buffer Memory

C. Chat loaders

D. React Components

E. (Q)

F. Vector Stores

Discussion 0

Correct Answer: B,F Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #15

A Generative AI Engineer is developing a patient-facing healthcare-focused chatbot. If the patient's question is not a medical emergency, the chatbot should solicit more information from the patient to pass to the doctor's office and suggest a few relevant pre-approved medical articles for reading. If the patient's question is urgent, direct the patient to calling their local emergency services.
Given the following user input:
"I have been experiencing severe headaches and dizziness for the past two days." Which response is most appropriate for the chatbot to generate?

A. Please provide your age, recent activities, and any other symptoms you have noticed along with your headaches and dizziness.

B. Please call your local emergency services.

C. Headaches can be tough. Hope you feel better soon!

D. Here are a few relevant articles for your browsing. Let me know if you have questions after reading them.

Discussion 0

Correct Answer: B Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Question #16

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?

A. 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.

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.

C. 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.

D. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.

Discussion 0

Correct Answer: B Vote an answer

Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

Download Free Databricks Databricks-Generative-AI-Engineer-Associate Demo

Simply submit your e-mail address below to get started with our free demo of your Databricks Databricks-Generative-AI-Engineer-Associate exam.