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Instant Download Databricks : Databricks-Generative-AI-Engineer-Associate Questions & Answers as PDF & Test Engine
- Exam Code: Databricks-Generative-AI-Engineer-Associate
- Exam Name: Databricks Certified Generative AI Engineer Associate
- Updated: Oct 24, 2026
- No. of Questions: 94 Questions and Answers
- Download Limit: Unlimited
Price: $69.98
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Databricks Databricks-Generative-AI-Engineer-Associate Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate Exam |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Passing Score: | 700/1000 or 70% |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45 |
| Exam Price: | USD 200 |
| Exam Format: | Multiple choice |
| Exam Duration: | 90 minutes |
| Available Languages: | Japanese, English, Korean, Brazilian Portuguese |
| Recommended Training: | Generative AI Engineering with Databricks |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluation and Monitoring | 12% | - Evaluate model and application performance - Iterate and improve solutions - Monitor application behavior and outputs |
| Application Development | 30% | - Use Databricks Vector Search - Integrate with MLflow - Build RAG applications - Develop LLM chains and workflows |
| Data Preparation | 14% | - Manage data quality and format - Prepare and process data for LLM use - Implement data pipelines and transformations |
| Design Applications | 14% | - Design solution architecture - Select appropriate models, tools and approaches - Decompose complex requirements into tasks |
| Governance | 8% | - Manage access and permissions - Apply Unity Catalog for data governance - Ensure compliance and security |
| Assembling and Deploying Apps | 22% | - Deploy models via Model Serving - Manage application lifecycle - Package and deploy applications |
What Candidates Ask About Databricks Certified Generative AI Engineer Associate
Databricks Certified Generative AI Engineer Associate is an official Databricks exam, registered under exam code Databricks-Generative-AI-Engineer-Associate. Passing it grants the Databricks Certified Generative AI Engineer Associate certification, a credential at the Associate level. Qualifications remain the quickest standardized proof of professional skill, and this one carries the vendor's own authority behind it.
The Databricks Certified Generative AI Engineer Associate exam presents 45 questions across 90 minutes. Attention fades over long stretches, which is exactly why exam pacing deserves deliberate practice: short, focused timed sessions in the Lead2Passed test engine teach you to spend your sharpest minutes wisely, flag generously, and never let one question consume the time meant for five.
Official registration for Databricks Certified Generative AI Engineer Associate costs USD 200, with 700/1000 or 70% needed to pass. Retakes charge the same USD 200 in full, so the financially sound move is confirming readiness before you book. Use the Lead2Passed practice tests across several spaced sessions; a passing score earned once might be luck, but a passing score earned repeatedly is readiness.
No formal prerequisites; recommended 6+ months hands-on experience building generative AI solutions
Policies change, so confirm the current requirements before you register through the official exam page.
Databricks Certified Generative AI Engineer Associate registration is available through the official channels listed below.
When scheduling, keep in mind the exam is delivered Online proctored or in-person test center.
Yes, Databricks recommends the following training for Databricks Certified Generative AI Engineer Associate candidates.
Training supplies the knowledge; deliberate practice supplies the exam skill. The 94 practice questions in the Lead2Passed Databricks-Generative-AI-Engineer-Associate package handle the second half.
There is. Lead2Passed offers a free PDF demo of the Databricks Certified Generative AI Engineer Associate questions so you can assess the material before paying. Once you buy, updates are free for 365 days, and after your product expires you can extend the update service at a 50% discount.
Lead2Passed backs your purchase with a 100% money-back guarantee under stated conditions. If you take the Databricks Certified Generative AI Engineer Associate exam within 60 days of purchase and fail, you may claim a full refund, provided the exam corresponds to your product. Attempts taken within 3 days of purchase are ineligible, as are products downloaded but never used, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Rather exchange than refund? You can receive two other exam products of equal value free and keep the update service on your original purchase.
Delivery is instant: files unlock for download at payment and are emailed to you within one minute. If nothing arrives within 2 hours, check spam and contact customer service. There is no limit on how many computers you may install the software on.
Databricks Certified Generative AI Engineer Associate is structured into 6 official domains. The leading ones are Evaluation and Monitoring (12%), Data Preparation (14%), and Assembling and Deploying Apps (22%). The full outline is published above on this page; knowing exactly what the exam tests is the foundation every efficient study plan is built on.
Databricks Certified Generative AI Engineer Associate Sample Questions:
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. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
- 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 the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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.
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?
- A. BGE-large
- B. Dolly 1.5B
- C. OpenAI GPT-4
- D. Llama2-70B
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?
- A. Ingest documents from a source - > Index the documents and saves to Vector Search - > User submits queries against an LLM - > LLM retrieves relevant documents - > Evaluate model - > LLM generates a response - > Deploy it using Model Serving
- B. Ingest documents from a source - > Index the documents and save to Vector Search - > User submits queries against an LLM - > LLM retrieves relevant documents - > LLM generates a response - > Evaluate model - > Deploy it using Model Serving
- C. Ingest documents from a source - > Index the documents and save to Vector Search - > Evaluate model
- > Deploy it using Model Serving - D. User submits queries against an LLM - > Ingest documents from a source - > Index the documents and save to Vector Search - > LLM retrieves relevant documents - > LLM generates a response - > Evaluate model - > Deploy it using Model Serving
Correct Answer: B 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?
- A. Add credentials using environment variables
- B. Use spark.conf.set ()
- C. Pass the secrets in plain text
- D. Pass variables using the Databricks Feature Store API
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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