GENERATIVE-AI-ENGINEER-ASSOCIATE Real Exam Questions
Databricks Certified Generative AI Engineer Associate. Everything you need to prepare, practice, and pass.
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86
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Certification Overview
This exam focuses on practical implementation of retrieval-augmented generation (RAG) systems, vector search and embeddings, foundation model selection and optimization, and prompt engineering techniques. It emphasizes real-world considerations like cost optimization and building complete, deployable Gen AI applications rather than theoretical foundations.
What This Certification Proves
This certification validates practical expertise in building generative AI applications using Databricks tools and best practices. It demonstrates proficiency in implementing RAG systems, selecting appropriate foundation models, and optimizing costs—essential skills for engineers deploying production Gen AI solutions.
Who Should Take This Exam
Mid-career developers, data engineers, and ML engineers with 1-3 years of experience looking to specialize in generative AI applications. Ideal for professionals transitioning from traditional ML/data engineering to Gen AI, or those already building Gen AI systems who want formal validation.
Topic Breakdown
86 domains covering 99 questions
| Domain | Questions | Weight |
|---|---|---|
| Prompt Engineering | 6 | 6% |
| Generative Ai Application Design | 3 | 3% |
| Generative Ai Application Development | 3 | 3% |
| Rag System Design | 2 | 2% |
| Building And Optimizing Rag Applications | 2 | 2% |
| Llm Application Security | 2 | 2% |
| Llm Application Design | 2 | 2% |
| Building Generative Ai Applications | 1 | 1% |
| Building Rag Applications | 1 | 1% |
| Building Retrieval-Augmented Generation (Rag) Applications | 1 | 1% |
| Configuring And Debugging Llm Agents | 1 | 1% |
| Data Governance For Llms | 1 | 1% |
| Data Ingestion And Preparation For Vector Search | 1 | 1% |
| Data Ingestion And Preprocessing For Generative Ai | 1 | 1% |
| Data Ingestion For Rag | 1 | 1% |
| Data Management And Optimization In Databricks Lakehouse | 1 | 1% |
| Data Preparation For Rag And Vector Search | 1 | 1% |
| Debugging Generative Ai Agents | 1 | 1% |
| Designing And Optimizing Rag Applications | 1 | 1% |
| Designing Generative Ai Applications For User Engagement | 1 | 1% |
| Ensuring Generative Ai Output Quality And Safety | 1 | 1% |
| Evaluating Rag Systems For Improvement | 1 | 1% |
| Foundation Model Management And Operationalization | 1 | 1% |
| Generative Ai Application Architecture | 1 | 1% |
| Generative Ai Mlops | 1 | 1% |
| Generative Ai Model Capabilities And Selection | 1 | 1% |
| Generative Ai Model Selection | 1 | 1% |
| Generative Ai Model Selection And Application | 1 | 1% |
| Generative Ai Model Selection And Evaluation | 1 | 1% |
| Generative Ai Solution Design And Optimization | 1 | 1% |
| Generative Ai System Architecture | 1 | 1% |
| Implementing Llm Application Safety And Moderation | 1 | 1% |
| Interacting With Databricks Foundation Model Endpoints | 1 | 1% |
| Llm Agent Design | 1 | 1% |
| Llm Agent System Design | 1 | 1% |
| Llm Application Architecture | 1 | 1% |
| Llm Application Architecture And Workflow Design | 1 | 1% |
| Llm Application Design And Model Selection | 1 | 1% |
| Llm Application Design And Prompt Engineering | 1 | 1% |
| Llm Application Development And Optimization | 1 | 1% |
| Llm Application Development And Orchestration | 1 | 1% |
| Llm Application Security And Safety | 1 | 1% |
| Llm Customization And Control | 1 | 1% |
| Llm Deployment And Cost Management | 1 | 1% |
| Llm Evaluation Metrics | 1 | 1% |
| Llm Model Selection | 1 | 1% |
| Llm Model Selection And Capabilities | 1 | 1% |
| Llm Output Control | 1 | 1% |
| Llm Production Deployment And Scaling | 1 | 1% |
| Llm Security And Safety | 1 | 1% |
| Ml Model Deployment And Mlops | 1 | 1% |
| Mlops | 1 | 1% |
| Mlops And Model Governance On Databricks | 1 | 1% |
| Model Deployment And Mlops On Databricks | 1 | 1% |
| Model Deployment And Monitoring | 1 | 1% |
| Monitoring And Operationalizing Llm Applications | 1 | 1% |
| Natural Language Processing (Nlp) With Llms | 1 | 1% |
| Operationalizing And Monitoring Llm Applications | 1 | 1% |
| Optimizing Llm Prompts For Desired Output | 1 | 1% |
| Prompt Engineering Best Practices | 1 | 1% |
| Prompt Engineering For Llm Applications | 1 | 1% |
| Prompt Engineering Fundamentals | 1 | 1% |
| Rag Application Optimization | 1 | 1% |
| Rag Data Processing And Chunking | 1 | 1% |
| Rag Pipeline Design And Optimization | 1 | 1% |
| Rag System Architecture | 1 | 1% |
| Rag System Design And Optimization | 1 | 1% |
| Rag System Evaluation | 1 | 1% |
| Rag System Optimization And Evaluation | 1 | 1% |
| Rag System Quality And Safety | 1 | 1% |
| Real-Time Data Serving For Llm Applications | 1 | 1% |
| Responsible Ai | 1 | 1% |
| Responsible Ai Development | 1 | 1% |
| Responsible Ai Governance | 1 | 1% |
| Retrieval Augmented Generation (Rag) Architecture | 1 | 1% |
| Vector Database Management | 1 | 1% |
| Vector Search Indexing And Retrieval | 1 | 1% |
| Vector Search Performance Evaluation | 1 | 1% |
| Ai Agent Deployment And Monitoring | 1 | 1% |
| Vector Store Management And Indexing On Databricks | 1 | 1% |
| Ai Safety And Responsible Deployment | 1 | 1% |
| Ai System Safety And Responsible Ai Development | 1 | 1% |
| Ai/Ml Model Security | 1 | 1% |
| Building And Deploying Rag Applications | 1 | 1% |
| Building And Optimizing Rag Pipelines | 1 | 1% |
| Building Generative Ai Agents | 1 | 1% |
Study Plans
Choose a study plan that matches your schedule and experience level
30 Days
Intensive Sprint
Week 1-2
- Master fundamentals: Prompt Engineering
- Read Databricks official documentation
- Complete 4 questions daily
Week 3
- Deep dive: Generative Ai Application Design
- Review weak areas from results
- Take 2 full-length exams
Week 4
- Review all flagged questions
- Timed exams to build stamina
- Final revision of key concepts
60 Days
Balanced Approach
Week 1-2
- Survey all exam domains
- Set up study environment
- Begin with foundational topics
Week 3-4
- Focus: Prompt Engineering
- Focus: Generative Ai Application Design
- 2 questions daily
Week 5-6
- Focus: Generative Ai Application Development
- Hands-on labs if applicable
- Review explanations for wrong answers
Week 7-8
- Complete all 101 questions
- Identify and eliminate weak areas
- Take 3 full-length timed tests
90 Days
Comprehensive Study
Month 1
- Learn all exam domains at a comfortable pace
- Build strong foundational knowledge
- 2 questions daily
Month 2
- Deep dive into each domain
- Hands-on practice and labs
- Take weekly timed exams
Month 3
- Work through all 101 questions
- Identify and eliminate weak areas
- Take 3 full-length timed exams
GENERATIVE-AI-ENGINEER-ASSOCIATE-Specific Tips
- Deep-dive into RAG architectures—this is the core of the exam. Understand retrieval strategies, ranking, and re-ranking techniques.
- Get hands-on with vector search implementations (FAISS, Milvus, Pinecone equivalents). Know indexing strategies and similarity metrics.
- Master embedding models: understand trade-offs between different models (OpenAI, open-source), dimensionality, and when to fine-tune vs. use off-the-shelf.
- Practice prompt engineering systematically—learn prompt templates, few-shot techniques, and chain-of-thought patterns specific to different LLM applications.
- Study cost optimization in depth: understand token counting, batch processing, caching strategies, and model selection trade-offs between capability and cost.
- Review foundation model selection criteria: when to use fine-tuning vs. RAG vs. prompt engineering, and cost/performance implications of each.
- Build end-to-end Gen AI applications using Databricks during prep—focus on integrating multiple components (embeddings, retrieval, LLM, evaluation).
Relevant Career Roles
Sample Questions
Try 5 free questions from the GENERATIVE-AI-ENGINEER-ASSOCIATE question bank
A Generative AI Engineer is assessing the responses from a customer-facing GenAI application that they are developing to assist in selling automotive parts. The application requires the customer to explicitly input account_id and transaction_id to answer questions. After initial launch, the customer feedback was that the application did well on answering order and billing details, but failed to accurately answer shipping and expected arrival date questions. Which of the following receivers would improve the application's ability to answer these questions?
A Generative AI Engineer is creating a customer support bot that should respond differently to an end user based on the sentiment in their initial message. For example, if the end user's message was angry, the bot should try to de-escalate their negative sentiments as it solves the customer query. They want to make sure their approach follows best practices. Which approach will do this?
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that have been scanned and saved as image files in formats like .jpeg or .png. They want to develop a solution using the least amount of lines of code. Which Python package should be used to extract the text from the source documents?
A Generative AI Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios. Which authentication method should they choose?
Generative AI Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability in their RAG application. Which option will do this with the least effort and in the most performant way?
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