Loading exam details...

DatabricksAssociate

GENERATIVE-AI-ENGINEER-ASSOCIATE

Databricks Certified Generative AI Engineer Associate

Master the Databricks Certified Generative AI Engineer Associate exam with Real exam questions, expert-verified explanations, and detailed answer keys. Perfect for professionals advancing in the Databricks ecosystem.

4.7(53 ratings)

Real exam questionsExpert Explanations Included10 Sample Questions

Try 10 Free Questions Get Full Access

Quick Stats

VendorDatabricks

Sample Questions10 questions

30-day access$49.99 ($1.67/day)

90-day access$69.99 ($0.78/day) ✓

Real Exam Questions PDFLifetime · keep forever$19.99

Free Exam Brief

Topics, exam shape, and sample questions

Exam Domains & Topics

86 domains

Prompt Engineering6% · 6 Qs

Generative Ai Application Design3% · 3 Qs

Generative Ai Application Development3% · 3 Qs

Rag System Design2% · 2 Qs

Building And Optimizing Rag Applications2% · 2 Qs

Llm Application Security2% · 2 Qs

Llm Application Design2% · 2 Qs

Building Generative Ai Applications1% · 1 Qs

Building Rag Applications1% · 1 Qs

Building Retrieval-Augmented Generation (Rag) Applications1% · 1 Qs

Configuring And Debugging Llm Agents1% · 1 Qs

Data Governance For Llms1% · 1 Qs

Data Ingestion And Preparation For Vector Search1% · 1 Qs

Data Ingestion And Preprocessing For Generative Ai1% · 1 Qs

Data Ingestion For Rag1% · 1 Qs

Data Management And Optimization In Databricks Lakehouse1% · 1 Qs

Data Preparation For Rag And Vector Search1% · 1 Qs

Debugging Generative Ai Agents1% · 1 Qs

Designing And Optimizing Rag Applications1% · 1 Qs

Designing Generative Ai Applications For User Engagement1% · 1 Qs

Ensuring Generative Ai Output Quality And Safety1% · 1 Qs

Evaluating Rag Systems For Improvement1% · 1 Qs

Foundation Model Management And Operationalization1% · 1 Qs

Generative Ai Application Architecture1% · 1 Qs

Generative Ai Mlops1% · 1 Qs

Generative Ai Model Capabilities And Selection1% · 1 Qs

Generative Ai Model Selection1% · 1 Qs

Generative Ai Model Selection And Application1% · 1 Qs

Generative Ai Model Selection And Evaluation1% · 1 Qs

Generative Ai Solution Design And Optimization1% · 1 Qs

Generative Ai System Architecture1% · 1 Qs

Implementing Llm Application Safety And Moderation1% · 1 Qs

Interacting With Databricks Foundation Model Endpoints1% · 1 Qs

Llm Agent Design1% · 1 Qs

Llm Agent System Design1% · 1 Qs

Llm Application Architecture1% · 1 Qs

Llm Application Architecture And Workflow Design1% · 1 Qs

Llm Application Design And Model Selection1% · 1 Qs

Llm Application Design And Prompt Engineering1% · 1 Qs

Llm Application Development And Optimization1% · 1 Qs

Llm Application Development And Orchestration1% · 1 Qs

Llm Application Security And Safety1% · 1 Qs

Llm Customization And Control1% · 1 Qs

Llm Deployment And Cost Management1% · 1 Qs

Llm Evaluation Metrics1% · 1 Qs

Llm Model Selection1% · 1 Qs

Llm Model Selection And Capabilities1% · 1 Qs

Llm Output Control1% · 1 Qs

Llm Production Deployment And Scaling1% · 1 Qs

Llm Security And Safety1% · 1 Qs

Ml Model Deployment And Mlops1% · 1 Qs

Mlops1% · 1 Qs

Mlops And Model Governance On Databricks1% · 1 Qs

Model Deployment And Mlops On Databricks1% · 1 Qs

Model Deployment And Monitoring1% · 1 Qs

Monitoring And Operationalizing Llm Applications1% · 1 Qs

Natural Language Processing (Nlp) With Llms1% · 1 Qs

Operationalizing And Monitoring Llm Applications1% · 1 Qs

Optimizing Llm Prompts For Desired Output1% · 1 Qs

Prompt Engineering Best Practices1% · 1 Qs

Prompt Engineering For Llm Applications1% · 1 Qs

Prompt Engineering Fundamentals1% · 1 Qs

Rag Application Optimization1% · 1 Qs

Rag Data Processing And Chunking1% · 1 Qs

Rag Pipeline Design And Optimization1% · 1 Qs

Rag System Architecture1% · 1 Qs

Rag System Design And Optimization1% · 1 Qs

Rag System Evaluation1% · 1 Qs

Rag System Optimization And Evaluation1% · 1 Qs

Rag System Quality And Safety1% · 1 Qs

Real-Time Data Serving For Llm Applications1% · 1 Qs

Responsible Ai1% · 1 Qs

Responsible Ai Development1% · 1 Qs

Responsible Ai Governance1% · 1 Qs

Retrieval Augmented Generation (Rag) Architecture1% · 1 Qs

Vector Database Management1% · 1 Qs

Vector Search Indexing And Retrieval1% · 1 Qs

Vector Search Performance Evaluation1% · 1 Qs

Ai Agent Deployment And Monitoring1% · 1 Qs

Vector Store Management And Indexing On Databricks1% · 1 Qs

Ai Safety And Responsible Deployment1% · 1 Qs

Ai System Safety And Responsible Ai Development1% · 1 Qs

Ai/Ml Model Security1% · 1 Qs

Building And Deploying Rag Applications1% · 1 Qs

Building And Optimizing Rag Pipelines1% · 1 Qs

Building Generative Ai Agents1% · 1 Qs

What learners say

Feedback from people who studied GENERATIVE-AI-ENGINEER-ASSOCIATE on NerdExam.

The LLM deployment questions here are spot on.

jakub_plML engineer

The Mosaic AI and MLflow integration questions are well-targeted. Solid prep if you actually work in Databricks day to day.

rachelwSenior ML engineer

Still mid-study for the Generative AI Engineer cert. The questions are built around real Databricks workflows - Unity Catalog, model registries, vector search - and that is helping me connect the dots. Feeling much more confident about the evaluation and deployment domains now.

hans_deSoftware engineer

I'll be honest - some questions felt a bit easier than what I saw on the actual exam, especially around fine-tuning. But the coverage across responsible AI, model serving, and LangChain integration was genuinely useful. I passed, so I can't complain.

alyssa_dAI consultant

RAG finally clicked. Passed on my first attempt.

kev92Data engineer

I was shaky on vector search and embedding concepts going in. The practice questions here forced me to actually understand them, not just memorize.

layla.egData scientist

Free Practice

Try 10 Free Questions

Full interactive quiz with detailed explanations, score analysis, and weak topic identification. No sign-up required.

Start Free Quiz

86 exam domainsExpert-verified explanations

What you get with full access

Multiple Question Types

Single choice, multi-select, drag-and-drop, ordering, hotspot, and more.

Detailed Explanations

Expert-verified explanations for every question to help you learn, not just memorize.

Score Analysis

Domain-level breakdown showing exactly where you need to focus.

Timed Exam Mode

Simulate exam-style conditions with configurable time limits.

Real Exam Questions PDF

Downloadable bank of real exam questions with full topic coverage and detailed explanations.

Progress Tracking

Track your mastery over time with adaptive learning recommendations.

Frequently bought together

Associate

DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK

Databricks Certified Associate Developer for Apache Spark

150+ questions

Databricks

DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST

Databricks Certified Professional Data Scientist

Real exam questions

Databricks

CERTIFIED-DATA-ENGINEER-PROFESSIONAL

Databricks Certified Data Engineer Professional

Real exam questions

Associate

CERTIFIED-DATA-ANALYST-ASSOCIATE

Databricks Certified Data Analyst Associate

Real exam questions

nerdexam

Loading exam details...

nerdexam

DatabricksAssociate

GENERATIVE-AI-ENGINEER-ASSOCIATE

Databricks Certified Generative AI Engineer Associate

4.7(53 ratings)

Real exam questionsExpert Explanations Included10 Sample Questions

Try 10 Free Questions Get Full Access

Quick Stats

VendorDatabricks

Sample Questions10 questions

30-day access$49.99 ($1.67/day)

90-day access$69.99 ($0.78/day) ✓

Real Exam Questions PDFLifetime · keep forever$19.99

Free Exam Brief

Topics, exam shape, and sample questions

Exam Domains & Topics

86 domains

Prompt Engineering6% · 6 Qs

Generative Ai Application Design3% · 3 Qs

Generative Ai Application Development3% · 3 Qs

Rag System Design2% · 2 Qs

Building And Optimizing Rag Applications2% · 2 Qs

Llm Application Security2% · 2 Qs

Llm Application Design2% · 2 Qs

Building Generative Ai Applications1% · 1 Qs

Building Rag Applications1% · 1 Qs

Building Retrieval-Augmented Generation (Rag) Applications1% · 1 Qs

Configuring And Debugging Llm Agents1% · 1 Qs

Data Governance For Llms1% · 1 Qs

Data Ingestion And Preparation For Vector Search1% · 1 Qs

Data Ingestion And Preprocessing For Generative Ai1% · 1 Qs

Data Ingestion For Rag1% · 1 Qs

Data Management And Optimization In Databricks Lakehouse1% · 1 Qs

Data Preparation For Rag And Vector Search1% · 1 Qs

Debugging Generative Ai Agents1% · 1 Qs

Designing And Optimizing Rag Applications1% · 1 Qs

Designing Generative Ai Applications For User Engagement1% · 1 Qs

Ensuring Generative Ai Output Quality And Safety1% · 1 Qs

Evaluating Rag Systems For Improvement1% · 1 Qs

Foundation Model Management And Operationalization1% · 1 Qs

Generative Ai Application Architecture1% · 1 Qs

Generative Ai Mlops1% · 1 Qs

Generative Ai Model Capabilities And Selection1% · 1 Qs

Generative Ai Model Selection1% · 1 Qs

Generative Ai Model Selection And Application1% · 1 Qs

Generative Ai Model Selection And Evaluation1% · 1 Qs

Generative Ai Solution Design And Optimization1% · 1 Qs

Generative Ai System Architecture1% · 1 Qs

Implementing Llm Application Safety And Moderation1% · 1 Qs

Interacting With Databricks Foundation Model Endpoints1% · 1 Qs

Llm Agent Design1% · 1 Qs

Llm Agent System Design1% · 1 Qs

Llm Application Architecture1% · 1 Qs

Llm Application Architecture And Workflow Design1% · 1 Qs

Llm Application Design And Model Selection1% · 1 Qs

Llm Application Design And Prompt Engineering1% · 1 Qs

Llm Application Development And Optimization1% · 1 Qs

Llm Application Development And Orchestration1% · 1 Qs

Llm Application Security And Safety1% · 1 Qs

Llm Customization And Control1% · 1 Qs

Llm Deployment And Cost Management1% · 1 Qs

Llm Evaluation Metrics1% · 1 Qs

Llm Model Selection1% · 1 Qs

Llm Model Selection And Capabilities1% · 1 Qs

Llm Output Control1% · 1 Qs

Llm Production Deployment And Scaling1% · 1 Qs

Llm Security And Safety1% · 1 Qs

Ml Model Deployment And Mlops1% · 1 Qs

Mlops1% · 1 Qs

Mlops And Model Governance On Databricks1% · 1 Qs

Model Deployment And Mlops On Databricks1% · 1 Qs

Model Deployment And Monitoring1% · 1 Qs

Monitoring And Operationalizing Llm Applications1% · 1 Qs

Natural Language Processing (Nlp) With Llms1% · 1 Qs

Operationalizing And Monitoring Llm Applications1% · 1 Qs

Optimizing Llm Prompts For Desired Output1% · 1 Qs

Prompt Engineering Best Practices1% · 1 Qs

Prompt Engineering For Llm Applications1% · 1 Qs

Prompt Engineering Fundamentals1% · 1 Qs

Rag Application Optimization1% · 1 Qs

Rag Data Processing And Chunking1% · 1 Qs

Rag Pipeline Design And Optimization1% · 1 Qs

Rag System Architecture1% · 1 Qs

Rag System Design And Optimization1% · 1 Qs

Rag System Evaluation1% · 1 Qs

Rag System Optimization And Evaluation1% · 1 Qs

Rag System Quality And Safety1% · 1 Qs

Real-Time Data Serving For Llm Applications1% · 1 Qs

Responsible Ai1% · 1 Qs

Responsible Ai Development1% · 1 Qs

Responsible Ai Governance1% · 1 Qs

Retrieval Augmented Generation (Rag) Architecture1% · 1 Qs

Vector Database Management1% · 1 Qs

Vector Search Indexing And Retrieval1% · 1 Qs

Vector Search Performance Evaluation1% · 1 Qs

Ai Agent Deployment And Monitoring1% · 1 Qs

Vector Store Management And Indexing On Databricks1% · 1 Qs

Ai Safety And Responsible Deployment1% · 1 Qs

Ai System Safety And Responsible Ai Development1% · 1 Qs

Ai/Ml Model Security1% · 1 Qs

Building And Deploying Rag Applications1% · 1 Qs

Building And Optimizing Rag Pipelines1% · 1 Qs

Building Generative Ai Agents1% · 1 Qs

What learners say

Feedback from people who studied GENERATIVE-AI-ENGINEER-ASSOCIATE on NerdExam.

The LLM deployment questions here are spot on.

jakub_plML engineer

The Mosaic AI and MLflow integration questions are well-targeted. Solid prep if you actually work in Databricks day to day.

rachelwSenior ML engineer

Still mid-study for the Generative AI Engineer cert. The questions are built around real Databricks workflows - Unity Catalog, model registries, vector search - and that is helping me connect the dots. Feeling much more confident about the evaluation and deployment domains now.

hans_deSoftware engineer

I'll be honest - some questions felt a bit easier than what I saw on the actual exam, especially around fine-tuning. But the coverage across responsible AI, model serving, and LangChain integration was genuinely useful. I passed, so I can't complain.

alyssa_dAI consultant