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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Application Development | 30% | - Implement prompt engineering - Develop LLM chains - Integrate Databricks tools (LangChain, etc.) |
| Governance | 8% | - Apply safety filters and guidelines - Manage permissions and logging |
| Data Preparation | 14% | - Manage data quality and chunking - Prepare data for RAG applications |
| Evaluation and Monitoring | 12% | - Monitor deployed applications - Evaluate model quality and performance |
| Assembling and Deploying Apps | 22% | - Package and deploy models - Use Vector Search - Build and deploy RAG pipelines |
| Design Applications | 14% | - Design and architect applications - Select appropriate models - Define functional requirements |
Databricks Databricks-Generative-AI-Engineer-Associate Exam FAQ — What Candidates Ask Most
The Databricks-Generative-AI-Engineer-Associate exam is the official Databricks exam for Databricks Certified Generative AI Engineer Associate. Passing it earns you the Generative AI Engineer credential, which sits at the Associate level. It is also connected to Databricks Certified Data Engineer Associate, Databricks Certified Machine Learning Associate, so what you learn carries over if you plan to pursue those paths as well. Employers treat the certification as verified proof of skill, which is why structured preparation with ActualTestsIT's practice questions pays off.
The Databricks-Generative-AI-Engineer-Associate exam contains 45 questions, and the time limit is 90 minutes. Translate that into a pacing plan before exam day: know roughly how long you can afford per item, flag anything that stalls you, and circle back at the end instead of burning minutes. Two or three full timed sessions in ActualTestsIT's test engine will teach you that rhythm far better than untimed reading ever will.
The passing score for the Databricks-Generative-AI-Engineer-Associate exam is Scaled Score (approx. 70%), and the official registration fee is $200 USD. Remember that a failed attempt means paying that fee again in full for a retake, so treat self-assessment as part of the budget: run ActualTestsIT's practice questions under timed conditions and only book your seat once your scores sit comfortably above the passing bar.
None (Recommended: 6+ months hands-on experience) Eligibility details can change, so before you register, confirm the current requirements on the official exam page: https://www.databricks.com/learn/certification/genai-engineer-associate.
Yes. ActualTestsIT offers a free PDF demo of the Databricks Certified Generative AI Engineer Associate practice questions on the samples page, so you can judge the format and quality yourself before paying anything. Every purchase also includes 365 days of free updates, and after the product expires you can extend the update service at a 50% discount from within your member zone.
If you take the corresponding exam within 60 days of purchase and do not pass, ActualTestsIT backs you with a 100% money-back guarantee. To claim it, submit a scanned copy of your exam enrollment slip together with your official Score Report PDF within 2 days of the exam, and the refund is processed within 7 days. The guarantee does not apply to attempts made within the first 3 days after purchase, to products downloaded without actually sitting the exam, or to free materials and expired orders, and the candidate name must match the payer's name. If you would rather have study material than money, you can instead exchange for two additional exam products of equal value, free of charge, while keeping the update service on your original purchase. Delivery itself is instant: your download is available right after payment and a copy reaches your mailbox within one minute — if nothing arrives within 2 hours, contact customer service. You may install the product on as many computers as you need.
The official Databricks Certified Generative AI Engineer Associate blueprint is organized into 6 domains. The leading areas are:
- Evaluation and Monitoring — 12% of the exam
- Data Preparation — 14% of the exam
- Assembling and Deploying Apps — 22% of the exam
For the complete domain-by-domain breakdown with every subtopic, see the Exam Topics outline above.
Databricks Certified Generative AI Engineer Associate Sample Questions:
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?
- A. context length 512: smallest model is 0.13GB and embedding dimension 384
- B. context length 32768: smallest model is 14GB and embedding dimension 4096
- C. context length 514; smallest model is 0.44GB and embedding dimension 768
- D. context length 2048: smallest model is 11GB and embedding dimension 2560
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user ' s query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.
Which change could the Generative Al Engineer perform to mitigate this issue?
- A. Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.
- B. Tune the chunk size of news articles or experiment with different embedding models.
- C. Split the LLM output by newline characters to truncate away the summarization explanation.
- D. Provide few shot examples of desired output format to the system and/or user prompt.
Correct Answer: D 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
What is an effective method to preprocess prompts using custom code before sending them to an LLM?
- A. It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts
- B. Rather than preprocessing prompts, it's more effective to postprocess the LLM outputs to align the outputs to desired outcomes
- C. Directly modify the LLM's internal architecture to include preprocessing steps
- D. Write a MLflow PyFunc model that has a separate function to process the prompts
Correct Answer: D 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
Databricks offers a number of built-in AI judges that provide metrics and rationale for different types of quality issues a Generative AI application may have.
Which of the following pairs of judges both require a ground-truth label in the evaluation dataset field expected_response to execute?
- A. context_sufficiency, correctness.
- B. correctness, groundedness.
- C. relevance_to_query, chunk_relevance.
- D. guideline_adherence, chunk_relevance.
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A Generative AI Engineer is building an interactive catalog for a company's inventory system that allows users to search for any item using a plain-text description. There are currently about 17,000 items, and new items are not frequently added. They need a solution that will be the most cost-effective and easy for the company to maintain.
Which solution should the engineer choose?
- A. Standard vector search with Databricks-managed embeddings and a Delta Sync index, triggered sync.
- B. Standard vector search with Databricks-managed embeddings and a Delta Sync index, continuous sync.
- C. Storage-optimized vector search with a Direct Vector Access index, triggered sync.
- D. Standard vector search with self-managed embeddings and a Delta Sync index, continuous sync.
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
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