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 #1

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?

A. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.

B. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.

C. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

D. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

Discussion 0

Correct Answer: C Vote an answer

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Question #2

A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?

A. First create a unique identifier for each document, then save to a Delta table

B. Split the data into train and test set, create a unique identifier for each document, then save to a Delta table

C. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table

D. Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

Discussion 0

Correct Answer: C Vote an answer

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Question #3

A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?

A. Diversity of responses

B. Randomness

C. Lack of relevance

D. Repetition of responses

Discussion 0

Correct Answer: A Vote an answer

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Question #4

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?

A. Operational metrics like request volume, latency, and errors

B. Both operational and quality metrics

C. Quality metrics like correctness and guideline adherence

D. No metrics are automatically captured

Discussion 0

Correct Answer: A Vote an answer

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Question #5

A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

A. AutoML

B. DatabrickslQ

C. Foundation Model APIs

D. Feature Serving

Discussion 0

Correct Answer: D Vote an answer

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Question #6

A Generative Al Engineer is developing a RAG application and would like to experiment with different embedding models to improve the application performance.
Which strategy for picking an embedding model should they choose?

A. pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace

B. Pick an embedding model with multilingual support to support potential multilingual user questions

C. Pick the most recent and most performant open LLM released at the time

D. Pick an embedding model trained on related domain knowledge

Discussion 0

Correct Answer: D Vote an answer

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Question #7

A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "[email protected]", "order_id": "RE987D"}

B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.

C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.

D. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.

Discussion 0

Correct Answer: A Vote an answer

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Question #8

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author's web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user's query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.
Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

A. Change embedding models and compare performance.

B. Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

C. Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.
Choose the strategy that gives the best performance metric.

D. Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

E. Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

Discussion 0

Correct Answer: B,C Vote an answer

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