As 2026 brings new chances and challenges, the Databricks-Generative-AI-Engineer-Associate credential remains a worthwhile target — and Pass4suresVCE remains the practical route, with verified Databricks Certified Generative AI Engineer Associate material, a self-paced engine, and delivery about a minute after you order.
Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Exam Duration: | 90 minutes |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate |
| Exam Format: | Multiple choice, Multiple select |
| Exam Price: | USD 200 |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45-60 |
| Recommended Training: | Databricks Academy - Generative AI Courses Mosaic AI Documentation |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: |  |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Databricks Mosaic AI Platform | - MLflow for GenAI
|
| Topic 2: Retrieval-Augmented Generation (RAG) | - RAG architecture
|
| Topic 3: Responsible AI and Governance | - Model governance
|
| Topic 4: Foundations of Generative AI | - Large Language Models (LLMs)
|
| Topic 5: Prompt Engineering | - Prompt evaluation
|
Preparing for Databricks-Generative-AI-Engineer-Associate on a Busy Schedule
- Databricks Mosaic AI Platform ()
- Retrieval-Augmented Generation (RAG) ()
- Prompt Engineering ()
Databricks Certified Generative AI Engineer Associate Sample Questions:
A Generative AI Engineer has set up an endpoint with AI Guardrails turned on to block any incoming requests that divulge PII. They also have inference tables enabled for this endpoint. If an end user sends their phone number in their prompt, what will appear in the inference table for that record?
- A. The full answer that would have been sent without the PII block will appear in inference tables.
- B. The request will appear, but the response column will be blank.
- C. The record will not appear in inference tables because it contains PII.
- D. The request will appear, but the response will be the error indicating that the request was blocked due to PII.
Correct Answer: D 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output "In Stock" if the product is available or only the term "Out of Stock" if not.
Which prompt will work to allow the engineer to respond to call classification labels correctly?
- A. You will be given a customer call transcript where the customer asks about product availability. The outputs are either "In Stock" or "Out of Stock". Format the output in JSON, for example: {"call_id":
"123", "label": "In Stock"}. - B. Respond with "Out of Stock" if the customer asks for a product.
- C. Respond with "In Stock" if the customer asks for a product.
- D. You will be given a customer call transcript where the customer inquires about product availability.
Respond with "In Stock" if the product is available or "Out of Stock" if not.
Correct Answer: A 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
A Generative Al Engineer is building a production-ready LLM system which replies directly to customers.
The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?
- A. Host Llama Guard on Foundation Model API and use it to detect unsafe responses
- B. Add some LLM calls to their chain to detect unsafe content before returning text
- C. Add a regex expression on inputs and outputs to detect unsafe responses.
- D. Ask users to report unsafe responses
Correct Answer: A 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
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 or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct 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. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
- D. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
Correct Answer: C 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
A Generative AI Engineer needs to allocate costs for an agent deployed via Agent Framework behind AI Gateway. The finance team requires daily reporting by workspace and endpoint, including token usage and request counts. The engineer wants to query this data with SQL and join it to an internal cost-center mapping table.
Which table type best serves their needs?
- A. AI Gateway usage table.
- B. System Serving, Served Entities table.
- C. Inference Tables.
- D. MLflow experiment run tables.
Correct Answer: A 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
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