Databricks · DCGAE
Databricks Certified Generative AI Engineer Associate Practice Tests
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
Practice Questions
620
≈ 13 practice exams
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. The full bank has 620 questions, enough for 13 full-length practice exams.
Preview — answers shown1. A data engineering team has built a recommendation model that consumes features derived from user activity tables and product inventory tables stored in Unity Catalog. After the model is deployed to a serving endpoint, the data governance team asks which upstream tables contribute to the features used by the live endpoint. Which capability provides this answer automatically without requiring the team to maintain manual documentation? (Select one!)
AMLflow experiment tags set during training runs to record source table names alongside metric logs
BA custom Databricks audit log query filtering access events by table name to reconstruct which tables were read during feature materialization
CUnity Catalog automated data lineage, which tracks the full graph from source tables through the Feature Store to the serving endpoint
DInference Tables configured on the serving endpoint, whose captured request payloads can be reverse-engineered to infer upstream data origins
Explanation
Unity Catalog automatically tracks data lineage across the full pipeline — from source tables, through Feature Store transformations and materialization, to the serving endpoint — and surfaces this as a queryable lineage graph. The governance team can navigate this graph natively without any manual documentation effort, and the lineage remains accurate as the pipeline evolves. MLflow experiment tags require engineers to manually record table names and quickly become stale or incomplete when pipelines change. Inference Tables capture the inputs and outputs of live inference requests but have no visibility into where those input features originated upstream in the data pipeline. Custom audit log analysis requires significant engineering effort to reconstruct lineage and must be repeated each time the governance team needs to answer the question.
2. A Generative AI Engineer at Meridian Healthcare is building an LLM-powered documentation assistant for clinical staff. The organization's compliance team requires that no patient personally identifiable information — including email addresses, Social Security Numbers, and phone numbers — may be transmitted to or returned from the language model. The protection must be implemented at the infrastructure layer without requiring application code changes. Which AI Gateway feature meets this requirement? (Select one!)
ARate limiting at the endpoint level to throttle request frequency and reduce exposure risk
BSafety filters configured to block requests containing medically sensitive terminology
CPII detection and redaction to replace personal identifiers with placeholders in requests and responses
DTraffic splitting to redirect sensitive queries to an on-premises model endpoint
Explanation
PII detection and redaction in AI Gateway automatically scans incoming requests and outgoing responses for personal identifiers such as email addresses, Social Security Numbers, and phone numbers, replacing them with neutral placeholders before content reaches or leaves the language model. This operates at the infrastructure layer with no application code changes required. Safety filters are designed to block harmful content categories such as hate speech, violence, and sexual content; they do not identify or redact personal identifiers. Rate limiting controls request frequency and protects against cost overruns and SLA violations, but it has no mechanism to inspect or sanitize the data contents of requests. Traffic splitting routes requests between endpoints based on defined weights but cannot inspect payload contents to detect or remove PII.
3. A Generative AI Engineer at Crestview ML is training a large transformer model for enterprise document classification and wants to log the final trained checkpoint as an MLflow artifact for reproducibility. The checkpoint is stored as a single file totaling 7 GB. What should the engineer expect when they attempt to upload this file? (Select one!)
AThe upload will succeed because MLflow automatically compresses large artifacts before writing them to storage
BThe upload will fail because MLflow enforces a maximum artifact file size of 5 GB per upload
CThe upload will succeed but MLflow will silently truncate the file to 5 GB to conform to the storage limit
DThe upload will succeed but the artifact will only be accessible from within the same Databricks workspace and cannot be downloaded externally
Explanation
MLflow enforces a 5 GB per-artifact upload and download size limit. A 7 GB checkpoint file exceeds this ceiling, and the upload attempt will fail with an error before completing. MLflow does not perform automatic compression of artifact files before storage; the file is handled as-is, and the size constraint applies to the raw file size. Silent truncation does not occur; MLflow would never silently corrupt an artifact by trimming it to fit a limit. The behavior is consistent regardless of the storage backend — whether artifacts are written to DBFS, Unity Catalog Volumes, or Amazon S3, the 5 GB limit remains in effect. The engineer should consider splitting the checkpoint into shards, using a model registration workflow that bypasses this path, or writing the large file directly to object storage outside the MLflow artifact logging API.
4. A team at Streamline Analytics is deploying an LLM application using Foundation Model APIs. During the proof-of-concept phase, request volume is low and unpredictable. After launch, the application is expected to handle thousands of production requests per second continuously. Which serving configuration should the team use at each phase? (Select one!)
APay-per-token throughout both phases because it provides unlimited automatic scaling regardless of traffic volume.
BPay-per-token during the proof-of-concept phase and provisioned throughput in production, since pay-per-token suits low and unpredictable traffic while provisioned throughput ensures guaranteed capacity for high-volume workloads.
CProvisioned throughput during proof-of-concept to lock in predictable pricing, then pay-per-token in production so the application scales automatically with demand.
DProvisioned throughput throughout both phases to maintain consistent latency from development through production.
Explanation
Pay-per-token billing is recommended for beginning and low-volume phases because it charges only for tokens consumed, making it cost-effective when traffic is irregular and reserved capacity would sit idle. Provisioned throughput is recommended for all production workloads because it provides guaranteed reserved capacity, enabling throughput exceeding 25,000 queries per second with less than 50ms overhead latency, which pay-per-token cannot guarantee under sustained high load. Reversing this order means paying for reserved capacity during testing when it largely goes underutilized. Using pay-per-token throughout production risks throttling and inconsistent latency at scale since capacity is not reserved. Using provisioned throughput throughout development unnecessarily increases costs during the exploratory phase when traffic is low and unpredictable.
5. Contoso Financial has deployed an LLM-powered compliance assistant used by 3,000 employees across global time zones. Traffic is sustained and consistent throughout the day with no significant idle periods. The compliance team requires a guaranteed response time under two seconds, and the infrastructure budget must be fixed and predictable each month. Which Model Serving endpoint type should the team deploy? (Select one!)
AAn external model endpoint exposed through AI Gateway with burst rate limiting enabled
BA serverless custom model endpoint configured to scale to zero after 30 minutes of inactivity
CA pay-per-token Foundation Model API endpoint that auto-scales based on incoming request rate
DA provisioned throughput endpoint sized with dedicated model units for the expected sustained load
Explanation
Provisioned throughput endpoints allocate dedicated inference capacity expressed as model units, guaranteeing consistent sub-two-second latency under sustained high-concurrency load and generating a predictable fixed monthly cost that aligns with the team's budgetary constraints. Databricks explicitly recommends provisioned throughput for production workloads that require performance guarantees. Pay-per-token Foundation Model API endpoints are not designed for high-throughput applications; they lack dedicated resources and latency guarantees under consistently heavy load, and their variable costs are tied to token consumption rather than a fixed monthly commitment. A serverless custom model endpoint that scales to zero introduces cold-start delays when traffic resumes, violating the two-second response-time guarantee, and is ill-suited to an always-on 3,000-user workload. An external model endpoint through AI Gateway introduces a dependency on a third-party provider with additional network round-trips and cannot deliver the reserved capacity needed to maintain guaranteed response times at this scale.
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