The Databricks Certified Generative AI Engineer Associate certification validates skills for designing and implementing LLM-powered applications using Databricks. The exam covers RAG applications, LLM chains, prompt engineering, model selection, Vector Search, Model Serving, MLflow, and Unity Catalog.
What Is the Generative AI Engineer Associate Exam?
This certification is aimed at professionals who build and deploy generative AI solutions. Candidates should understand how to select models, prepare data, create RAG applications, evaluate results, and deploy AI solutions on Databricks.
Key Exam Details
- Certification: Databricks Certified Generative AI Engineer Associate
- Questions: 45 scored questions
- Duration: 90 minutes
- Format: Multiple-choice and multiple-select
- Prerequisite: None
- Recommended Experience: Around 6 months of hands-on experience
- Validity: 2 years
- Registration Fee: $200
Generative AI Engineer Associate Exam Topics
1. Data Preparation
Learn how data is prepared for generative AI applications.
Important areas include:
- Data cleaning
- Chunking
- Embeddings
- Metadata
- Retrieval preparation
- Vector databases
2. RAG Applications
Understand how Retrieval-Augmented Generation (RAG) works.
Study:
- Document retrieval
- Vector Search
- Similarity search
- Chunking strategies
- Retrieval quality
- Context optimization
3. Prompt Engineering and LLMs
Focus on:
- Prompt design
- System prompts
- Few-shot prompting
- LLM selection
- Model parameters
- Token limits
- LLM chains
4. Databricks AI Tools
Know the purpose of:
- Vector Search
- Model Serving
- MLflow
- Unity Catalog
- Agent Framework
- AI Gateway
These Databricks-specific technologies are central to the certification.
5. Evaluation and Monitoring
Learn how to evaluate and monitor generative AI applications.
Focus on:
- Evaluation metrics
- Ground-truth evaluation
- MLflow evaluation
- Tracing
- Inference logging
- Agent Monitoring
- Cost monitoring
The current exam guide also includes evaluating agent performance and monitoring deployed LLM applications.
6. Governance and Security
Understand responsible deployment of GenAI applications.
Study:
- Unity Catalog
- Data governance
- Access control
- Guardrails
- Data protection
- Model and inference costs
How to Prepare
1. Learn RAG Fundamentals
Understand the complete flow:
Documents → Chunking → Embeddings → Vector Search → Retrieval → LLM → Response
2. Practice Databricks Tools
Get hands-on experience with Vector Search, Model Serving, MLflow, and Unity Catalog.
3. Study LLM Selection
Understand trade-offs involving:
- Accuracy
- Latency
- Cost
- Context length
- Model size
4. Practice Evaluation
Learn how to measure retrieval and response quality instead of relying only on intuition.
5. Work on a Small RAG Project
Building a simple RAG application can help connect the concepts and improve practical understanding.
Practice Questions
Q.1 What is the primary purpose of RAG?
A. Retrieve relevant information before generating a response
B. Increase database storage
C. Configure network routing
D. Replace all databases
Answer: A. Retrieve relevant information before generating a response
Q.2 What does Vector Search help with?
A. Semantic similarity search
B. Network monitoring
C. User authentication
D. File compression
Answer: A. Semantic similarity search
Q.3 Which Databricks tool is used for experiment tracking and ML lifecycle management?
A. MLflow
B. DNS
C. CloudTrail
D. Route 53
Answer: A. MLflow
Q.4 What is chunking used for in a RAG pipeline?
A. Dividing documents into manageable sections
B. Encrypting network traffic
C. Creating user accounts
D. Deploying virtual machines
Answer: A. Dividing documents into manageable sections
Q.5 Which Databricks feature is used for data governance?
A. Unity Catalog
B. Vector Search
C. Model Serving
D. MLflow
Answer: A. Unity Catalog
Frequently Asked Questions
What is the Databricks Generative AI Engineer Associate certification?
It validates the ability to design and implement LLM-enabled solutions using Databricks.
Is there a prerequisite?
No formal prerequisite is required, although Databricks recommends related training and hands-on experience.
What should I study first?
Start with RAG, embeddings, chunking, Vector Search, prompt engineering, LLM selection, and Databricks AI tools.
Is hands-on experience important?
Yes. Practical experience with RAG applications, Vector Search, model deployment, and evaluation can make the concepts easier to understand.
How long is the exam?
The current exam has a 90-minute time limit.
Final Thoughts
The Databricks Generative AI Engineer Associate certification focuses on practical GenAI development. Give special attention to RAG, data preparation, prompt engineering, Vector Search, LLM selection, Model Serving, MLflow, evaluation, and governance.
Hands-on practice combined with scenario-based questions is a strong way to prepare.
Start your Generative AI Engineer Associate practice test today.
Written By:Sudheer Kumar
Published on: 19/08/2026
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