Databricks · DCGAE
Databricks Certified Generative AI Engineer Associate Practice Test
Validates the ability to design, develop, and deploy LLM-powered solutions on Databricks, covering RAG application design and data preparation, prompt engineering and retrieval chains, model serving and deployment, evaluation and monitoring for quality and safety, and governance with Unity Catalog.
Exam Details
Questions
620
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Databricks Certified Generative AI Engineer Associate Practice Exam Preparation
Use this DCGAE practice exam to prepare for Databricks Certified Generative AI Engineer Associate with realistic questions, detailed explanations, and focused study modes. The practice bank includes 620 questions for Databricks DCGAE, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Application Development30%
Assembling and Deploying Applications22%
Design Applications14%
Data Preparation14%
Evaluation and Monitoring12%
Governance8%
Exam Overview
The Databricks Certified Generative AI Engineer Associate certification validates an individual's ability to design, develop, and deploy large language model (LLM)-powered solutions on the Databricks platform. The exam tests practical competency across the full generative AI engineering lifecycle, including decomposing complex requirements into multi-stage reasoning pipelines, selecting appropriate models from both open-source and proprietary ecosystems, and implementing retrieval-augmented generation (RAG) applications using Databricks-native tooling.
Certified professionals are expected to demonstrate hands-on proficiency with key Databricks technologies: Vector Search for semantic similarity and document retrieval, Model Serving for scalable endpoint deployment, MLflow for experiment tracking and lifecycle management, and Unity Catalog for data governance and access control. All machine learning code on the exam is in Python; SQL may appear for non-ML data manipulation tasks. The certification is valid for two years, after which recertification requires retaking the current version of the exam.
Who Should Take This Exam
This certification is designed for practitioners actively building and deploying AI systems in enterprise environments, including AI Engineers, Generative AI Engineers, LLM Engineers, AI Solution Architects, MLOps Engineers, and Data Scientists with a focus on LLM or RAG workflows. It is particularly well-suited for engineers who work within the Databricks ecosystem and need to demonstrate production-level competency in generative AI solution development.
Candidates should have at least six months of hands-on experience developing generative AI solutions, practical familiarity with Python-based ML pipelines, and working knowledge of frameworks such as LangChain or LangGraph. Experience with Databricks-specific tools—MLflow, Unity Catalog, Vector Search, and Model Serving—is strongly recommended before attempting the exam.
Prerequisites
There are no formal prerequisites required to register for this exam; any candidate may attempt it. However, Databricks strongly recommends at least six months of hands-on experience in generative AI solution development before sitting for the certification.
Recommended technical knowledge includes Python proficiency (especially for model pipelines and application orchestration), familiarity with LLM concepts such as context windows, tokenization, and prompt engineering techniques (zero-shot, few-shot, chain-of-thought), and practical experience with LangChain or similar orchestration frameworks. Candidates should also be comfortable using Databricks-native tools including MLflow for experiment tracking, Unity Catalog for governance, Vector Search for embedding-based retrieval, and Model Serving for endpoint deployment.
Exam Format
The exam consists of approximately 45 scored multiple-choice and multiple-select questions to be completed within 90 minutes. It is delivered as a proctored online exam, meaning candidates complete it remotely under live or automated proctoring; no external aids are permitted. The exam is available in English, Japanese, Brazilian Portuguese, and Korean. The registration fee is $200 USD (local taxes may apply).
The passing score is 70%. Databricks notes that exams may include additional unscored items used to gather statistical data for future exam development; these items are not identified and do not affect the final score, meaning the total number of questions delivered may be slightly higher than the 45 scored items. The certification remains valid for two years, after which candidates must retake the current exam version to recertify.
Skills Measured
- 1.Application Development (30%): Implementing LLM chains and RAG pipelines using LangChain and LangGraph; applying context injection, few-shot prompting, and agentic AI patterns; building Python-based orchestration pipelines; integrating with external APIs and databases for dynamic data retrieval.
- 2.Assembling and Deploying Applications (22%): Deploying solutions to Databricks Model Serving endpoints; packaging models using MLflow pyfunc; managing model lifecycle with MLflow Tracking and Model Registry; optimizing Vector Search endpoints; incorporating Foundation Model APIs; configuring inference tables for logging.
- 3.Design Applications (14%): Designing multi-stage reasoning pipelines; selecting between open-source and proprietary LLMs based on requirements; applying prompt engineering techniques including zero-shot, few-shot, prompt chaining, and meta-prompts; defining agent tools and safety optimization strategies; understanding system and user prompt roles.
- 4.Data Preparation (14%): Processing and ingesting documents for RAG pipelines; selecting chunking strategies and embedding models; storing and indexing vectors; applying filtering techniques; leveraging Delta Lake for structured data storage within generative AI workflows.
- 5.Evaluation and Monitoring (12%): Evaluating retriever and LLM performance using MLflow; implementing LLM-as-a-Judge approaches for automated scoring; tracking metrics such as context precision, toxicity, and perplexity; configuring continuous logging and inference request analysis for production monitoring.
- 6.Governance (8%): Applying Unity Catalog for data access control, auditing, and compliance; implementing ethical AI frameworks and guardrails; managing PII exposure risks and security controls; ensuring regulatory compliance for enterprise AI deployments.
Study Tips
- Complete the official Databricks Academy self-paced course 'Generative AI Engineering with Databricks' before attempting the exam — it maps directly to all six exam domains and includes hands-on notebooks using Databricks Community Edition.
- Build at least one end-to-end RAG application from scratch using Databricks Vector Search, MLflow, and Model Serving. The exam heavily emphasizes Application Development (30%) and Assembling & Deploying Apps (22%), so practical lab experience with these tools is essential.
- Study MLflow's evaluation capabilities in depth, including how to configure LLM-as-a-Judge scoring and batch evaluation runs. The Evaluation and Monitoring domain tests specific API usage and metric interpretation, not just conceptual knowledge.
- Review the Unity Catalog documentation focused on AI governance use cases — access control for model endpoints, audit logging, and securing inference pipelines. The Governance domain (8%) is often under-studied but has clearly defined testable content.
- Practice under timed conditions using third-party practice exams on platforms like Udemy or Whizlabs. With 45 questions in 90 minutes, you have about two minutes per question; scenario-based questions require quick identification of the correct Databricks tool or API for a given use case.
- Study the LangChain and LangGraph integration patterns specific to Databricks, including how chains are structured, how retrievers are connected to vector stores, and how agents are defined with tools. All ML code on the exam is Python, so be comfortable reading and interpreting LangChain pipeline code.
- Review Databricks Foundation Model APIs and the difference between external model endpoints and Databricks-hosted models. Understanding when to use pay-per-token Foundation Model APIs versus deploying custom models to serving endpoints is a recurring theme in the deployment and design domains.
Career Benefits
Professionals holding this certification are positioned for roles at the intersection of software engineering and applied AI, including Generative AI Engineer, LLM Engineer, AI Solution Architect, and MLOps Engineer. Generative AI engineering roles command some of the highest compensation in the technology sector, with average salaries reported around $214,000 annually in the United States; the certification directly signals enterprise-grade deployment skills that go beyond prototyping or research experience.
The generative AI applications market is projected to grow at a CAGR exceeding 46% through 2030, and employer demand for engineers who can bridge the gap between experimental LLM work and production-ready Databricks deployments continues to outpace supply. As Databricks is widely adopted across Fortune 500 companies for data and AI workloads, this certification carries strong recognition among employers already invested in the Databricks ecosystem. It complements other Databricks credentials (such as the Data Engineer Associate or ML Professional certifications) for practitioners building a comprehensive Databricks certification portfolio.
Sample Questions
5 sample questions with answers and explanations. Start a practice session to test yourself across all 620 questions.
Preview — answers shown1. A company configures rate limits on a Model Serving endpoint with the following: endpoint limit 1000 QPM, user_default 50 QPM, group data-science-team 200 QPM, and user alice@company.com 150 QPM. Alice is a member of data-science-team. What is the effective rate limit for Alice? (Select one!)
A50 QPM based on user_default
B1000 QPM based on endpoint limit
C200 QPM based on group limit
D150 QPM based on user-specific limit
Explanation
User-specific rate limits override both group limits and user_default settings in the rate limit hierarchy. Since Alice has an explicit user-specific limit of 150 QPM, this takes precedence over her group membership in data-science-team (200 QPM) and the user_default (50 QPM). The endpoint limit of 1000 QPM is the global maximum but does not apply when more restrictive user-specific limits exist. User-specific limits always have the highest priority in the rate limiting hierarchy.
2. An ML engineer is implementing a RAG application with parent-child chunking strategy. They create small chunks of 512 tokens for precise searching and maintain larger parent chunks of 2048 tokens for context. When a search returns relevant child chunks, they need to provide the full parent document to the LLM. Which retrieval approach supports this small-to-big retrieval pattern? (Select one!)
AConfigure Vector Search hybrid mode to automatically retrieve parent context for matched child chunks
BIndex only child chunks with parent document IDs as metadata and retrieve parent documents from the source Delta table
CIndex both child and parent chunks in separate Vector Search indexes and join results using primary keys
DUse semantic chunking to automatically create hierarchical chunk relationships in a single index
Explanation
Parent-child chunking requires indexing the small child chunks for precise searching while maintaining references to larger parent chunks. The correct approach is to index child chunks in Vector Search with parent document IDs stored as metadata fields. When relevant child chunks are retrieved, use the parent IDs to fetch full parent documents from the source Delta table for LLM context. Creating separate indexes adds complexity and requires manual joining logic. Semantic chunking creates semantically coherent chunks but does not implement parent-child relationships. Hybrid mode combines vector and keyword search but does not automatically retrieve parent context.
3. A data scientist is implementing context precision evaluation for a RAG application. They need to select an appropriate evaluation metric that assesses whether the retrieved context chunks are relevant to the user's query without requiring ground truth labels. Which MLflow LLM judge should they use? (Select one!)
ACorrectness
BRetrievalRelevance
CRetrievalSufficiency
DRetrievalGroundedness
Explanation
RetrievalRelevance is the correct judge for assessing context precision because it evaluates whether the retrieved documents are relevant to the user query and does not require ground truth. This directly measures the quality of the retrieval system. Correctness requires ground truth to compare the response against the expected answer. RetrievalSufficiency also requires ground truth to determine if the retrieved context contains sufficient information to answer the question. RetrievalGroundedness assesses whether the LLM response is grounded in the retrieved context (measures hallucination), not whether the context itself is relevant to the query.
4. A company is querying a Vector Search index that uses the databricks-gte-large-en embedding model. The index was created with L2 distance metric. To use cosine similarity for ranking, what must be done? (Select one!)
AUse the query_type parameter set to COSINE
BChange the distance metric parameter in the query to cosine
CNormalize embeddings before indexing and querying
DConfigure the index with cosine_similarity_enabled flag
Explanation
To use cosine similarity with Vector Search, embeddings must be normalized before both indexing and querying. When embeddings are normalized to unit length, L2 distance ranking becomes equivalent to cosine similarity ranking. There is no query parameter to change distance metrics at query time. Vector Search does not have a COSINE query_type parameter. There is no cosine_similarity_enabled flag for index configuration. The distance metric is fixed at index creation as L2, but normalization allows cosine similarity behavior.
5. A data engineering team is implementing a LangChain retriever using DatabricksVectorSearch for a legal document search application. The source Vector Search index uses self-managed embeddings with OpenAI ada-002 embeddings stored in a Delta table. Which configuration is required when instantiating the retriever? (Select one!)
AOnly index_name parameter is required since embeddings are pre-computed
Bindex_name and text_column parameters only
Cindex_name and embedding parameters with OpenAIEmbeddings instance
Dindex_name, embedding, and text_column parameters
Explanation
When using DatabricksVectorSearch with self-managed embeddings, you must provide three parameters: index_name to specify the Vector Search index, embedding with an instance of the same embedding model used during indexing (OpenAIEmbeddings in this case), and text_column to specify which column contains the document content. For Delta Sync indexes with Databricks-managed embeddings, only index_name is required. The same embedding model must be used for both indexing and querying to ensure vector compatibility.
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