- Exam Code: Databricks-Generative-AI-Engineer-Associate
- Exam Name: Databricks Certified Generative AI Engineer Associate
- Certification Provider: Databricks
- Corresponding Certification:Generative AI Engineer
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Starting from zero is still starting. In 2026, ITPassLeader guides Databricks Certified Generative AI Engineer Associate candidates from first question to exam room with Databricks-Generative-AI-Engineer-Associate practice questions that prioritize the knowledge that matters most.
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 |
| Exam Price: | USD 200 |
| Real Exam Qty: | 45 |
| Passing Score: | 700/1000 or 70% |
| Available Languages: | English, Brazilian Portuguese, Korean, Japanese |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple choice |
| 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 |
|---|---|---|
| Topic 1: Assembling and Deploying Apps | 22% | - Manage application lifecycle - Deploy models via Model Serving - Package and deploy applications |
| Topic 2: Design Applications | 14% | - Decompose complex requirements into tasks - Select appropriate models, tools and approaches - Design solution architecture |
| Topic 3: Governance | 8% | - Apply Unity Catalog for data governance - Ensure compliance and security - Manage access and permissions |
| Topic 4: Evaluation and Monitoring | 12% | - Iterate and improve solutions - Evaluate model and application performance - Monitor application behavior and outputs |
| Topic 5: Application Development | 30% | - Develop LLM chains and workflows - Build RAG applications - Use Databricks Vector Search - Integrate with MLflow |
| Topic 6: Data Preparation | 14% | - Implement data pipelines and transformations - Prepare and process data for LLM use - Manage data quality and format |
Ask Us Anything About Databricks-Generative-AI-Engineer-Associate
The Databricks-Generative-AI-Engineer-Associate exam is Databricks's path to the Databricks Certified Generative AI Engineer Associate certification, a credential at the Associate level. It confirms you can apply the Databricks Certified Generative AI Engineer Associate objectives in realistic scenarios — the kind of proof that changes job conversations. It's easier to want than to earn, which is exactly why structured practice beats good intentions.
The Databricks Certified Generative AI Engineer Associate blueprint spans 6 domains, led by Assembling and Deploying Apps (22%), Evaluation and Monitoring (12%), and Data Preparation (14%). Treat the weightings as your study budget — heavier domains earn heavier hours. The full topic list is in the exam topics section above; plan against it, not against guesswork.
The Databricks Certified Generative AI Engineer Associate exam packs 45 questions into 90 minutes. Convert that into a tempo before test day: minutes divided by questions is your per-item budget, and anything burning double its budget gets flagged and revisited at the end. Rehearse the tempo against a real timer — every ITPassLeader engine session enforces one — until finishing with review time to spare feels routine.
Passing Databricks Certified Generative AI Engineer Associate requires 700/1000 or 70%, and each attempt costs USD 200 — retakes included, at full price. The sound strategy is arriving over-prepared: drill with ITPassLeader practice questions until your timed scores clear the mark across several consecutive sessions, then register. Over-preparation is cheaper than re-registration.
Databricks lists the Databricks Certified Generative AI Engineer Associate question formats as: Multiple choice. Formats shape tactics — find the actual question sentence inside long scenario stems before reading the rest, and re-verify every selection on multi-answer items. ITPassLeader's 94 practice questions drill these formats until only the content feels new on exam day.
The Databricks-Generative-AI-Engineer-Associate exam is offered in English, Japanese, Brazilian Portuguese, Korean. Choose the language where your reading stays fastest under pressure — slow comprehension taxes every question in a timed exam. Testing in English? Daily sessions with ITPassLeader's English Databricks-Generative-AI-Engineer-Associate practice questions double as terminology training.
The certification earned through the Databricks Certified Generative AI Engineer Associate exam remains valid for 2 years. Note the expiry date the day you pass and research the recertification path early — a planned renewal is an errand, an expired credential is a project. Databricks owns these rules, so verify the current ones on the official certification page as your window approaches.
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Most candidates mix formats — PDF for reading, an engine for timed rehearsal.
Yes. ITPassLeader's free Databricks Certified Generative AI Engineer Associate PDF demo contains genuine sample questions with complete verified answers, so you can judge the quality firsthand. Every purchase includes 365 days of free updates, and renewing after expiry costs 50% of the regular price from your member zone. Inspect first — confident material has nothing to hide.
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Databricks Certified Generative AI Engineer Associate Sample Questions:
A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher-ranked documents. Which option should the team consider to optimize this workflow?
- A. Increase the number of documents added to the prompt to improve context relevance.
- B. Use a different embedding model for computing document embeddings.
- C. Use a reranker to order the documents based on the relevance scores.
- D. Modify the prompt to instruct the LLM to order the documents based on the relevance scores.
Correct Answer: C 🗳️
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A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM's response to achieve the desired response?
- A. Include few-shot examples in the prompt to the LLM
- B. Fine-tune the LLM on a dataset of desired tone and style
- C. Use a neutralizer to normalize the tone and style of the underlying documents
- D. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
Correct Answer: C 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
All of the following are Python APIs used to query Databricks foundation models. When running in an interactive notebook, which of the following libraries does not automatically use the current session credentials?
- A. Databricks Python SDK
- B. REST API via requests library
- C. OpenAI client
- D. MLflow Deployments SDK
Correct Answer: B 🗳️
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A Generative AI Engineer has deployed a customer-support agent in production that retrieves product documentation and generates responses. SMEs have been reviewing agent responses and providing feedback through a web interface that captures ratings of 1-5 stars and written comments. The engineer needs to systematically collect this feedback and use it to create an evaluation dataset that can be used to compare future agent versions against the current baseline performance.
Which approach should the engineer use to accomplish this task?
- A. Use the customer review app to collect SME feedback, then directly deploy the highest-rated responses as the new agent baseline without storing them as a formal evaluation dataset.
- B. Use Unity Catalog to create a view that filters only 5-star-rated interactions, then register this view as the evaluation dataset to benchmark all future agent versions.
- C. Export only the written SME comments to a text file and manually score them using a custom script, then use the script's output as the evaluation dataset for future agent comparisons.
- D. Log the SME ratings and comments directly to a Delta table with the corresponding user queries and agent responses, then use MLflow to create an evaluation dataset from this table and register it for future agent evaluations.
Correct Answer: D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?
- A. Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist
- B. Reduce the time that the users can interact with the LLM
- C. Ask the LLM to remind the user that the input is malicious but continue the conversation with the user
- D. Increase the amount of compute that powers the LLM to process input faster
Correct Answer: A 🗳️
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