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 machine learning engineer is logging a fine-tuned language model to MLflow with Unity Catalog registration. The model requires a custom embedding endpoint and preprocessing function. Which MLflow model flavor should they use? (Select one!)
Amlflow.sklearn with pipeline wrapper
Bmlflow.transformers with task parameter
Cmlflow.pyfunc for custom preprocessing logic
Dmlflow.langchain for LangChain integration
Explanation
The mlflow.pyfunc flavor is designed for custom Python models that require custom preprocessing, postprocessing, or integration with external services. The PythonModel class allows implementing load_context to load custom resources like embedding endpoints and predict to execute custom logic. The langchain flavor is for LangChain chains specifically. The transformers flavor works for Hugging Face models but has limited flexibility for custom preprocessing. The sklearn flavor is designed for scikit-learn models and would not appropriately handle language model requirements.
2. A company is preparing chat completion data for fine-tuning a Mistral-based foundation model. Their JSONL file contains conversation logs with system prompts, user messages, and assistant responses. After submission, the training run fails with a validation error. What is the most likely cause? (Select one!)
AMistral models do not support system role messages in the messages array
BThe last message in the messages array has role user instead of assistant
CThe messages array contains more than 10 exchanges per conversation
DThe training dataset contains fewer than 1.5 million tokens
Explanation
Mistral models do not support system role messages in chat completion data. The documentation explicitly states that while system messages must be first if included, Mistral models don't accept system roles at all. Having the last message as user instead of assistant would also cause failure since the last message must have role assistant, but the question asks for the most likely cause given they're using a Mistral model with system prompts. The 1.5 million token minimum applies to continued pre-training, not chat completion. There is no documented limit on the number of exchanges per conversation.
3. A data engineering team is setting up a Vector Search index source table that will store customer support transcripts with embeddings. The source table uses Unity Catalog and has Change Data Feed enabled. What is the maximum size for each row in the source Delta table? (Select one!)
A10 MB
B10 KB
C100 KB
D1 MB
Explanation
Vector Search Delta Sync indexes have a row size limit of 100 KB for source Delta tables. This limit applies to the total size of all columns in a row including metadata, text content, and embedding vectors. Rows exceeding 100 KB will cause sync failures. The limit is designed to ensure efficient index updates and query performance. 10 KB would be too restrictive for many use cases with rich metadata. 1 MB and 10 MB exceed the documented limits.
4. An ML team is evaluating different foundation models for a code generation application where quality is the top priority. They need to select the most appropriate Databricks-hosted Foundation Model API endpoint. Which model should they choose? (Select one!)
Adatabricks-meta-llama-3-3-70b-instruct for general-purpose code generation
Bdatabricks-dbrx-instruct for high-quality code completion
Cdatabricks-codellama-34b-instruct for specialized code generation
Ddatabricks-meta-llama-3-1-405b-instruct for complex reasoning tasks
Explanation
CodeLlama-34B is specifically optimized for code generation tasks and provides the highest quality for code-related workloads. Meta Llama 3.3-70B is for general chat and reasoning, not specialized for code. DBRX Instruct has been retired from Foundation Model APIs as of 2025. Meta Llama 3.1-405B is designed for complex reasoning tasks but is not specialized for code generation like CodeLlama. For code generation with quality priority, the specialized CodeLlama model is the optimal choice.
5. A company is creating a Vector Search index from a Delta table with 10 million documents. The embedding source column contains text that averages 12,000 tokens per document. The index creation fails. What is the MOST likely cause? (Select one!)
AThe embedding source column exceeds the maximum supported length of 8,192 tokens for databricks-gte-large-en
BThe table exceeds the maximum row size of 100KB
CThe Delta table does not have Change Data Feed enabled
DStorage-optimized endpoints do not support Delta Sync indexes
Explanation
The databricks-gte-large-en embedding model has a maximum context length of 8,192 tokens. Documents averaging 12,000 tokens exceed this limit, causing index creation to fail. The embedding model cannot process text beyond its token limit. Change Data Feed is required for standard endpoints but its absence would cause a different error message. Storage-optimized endpoints do support Delta Sync indexes with triggered sync mode. The 100KB row size limit applies to the entire row, not just the embedding column, and is less likely to be the primary issue with text-only content.
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