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 Generative AI Engineer at Holbrook Retail is designing a Foundation Model API architecture for a product catalog system. Two tasks must be supported: converting product descriptions into vector representations for an AI Search index, and generating personalized text explanations describing why a product matches a customer query. Which assignment of model types to tasks is correct? (Select one!)
AUse CodeLlama-34B to vectorize catalog descriptions because it handles structured data, and Llama 3.3 70B Instruct for generating explanations
BUse DBRX Instruct for both tasks, because large instruction-tuned models can produce indexable embeddings and generate text within the same inference call
CUse an embedding model such as BGE-large to vectorize product descriptions, and a generative model such as DBRX Instruct to produce customer-facing explanations
DUse BGE-large for both tasks, because it produces rich semantic representations that can also be decoded into natural language explanations
Explanation
Embedding models and generative models are fundamentally different in architecture and output type. BGE-large is an embedding model that converts text into fixed-dimensional dense vectors for similarity search and retrieval; it produces no text output and cannot generate product explanations of any kind. Generative models such as DBRX Instruct are trained to produce new text sequences and are the correct choice for generating personalized explanations. Assigning both tasks to BGE-large is incorrect because embedding models do not generate text. Assigning both tasks to DBRX Instruct is incorrect because generative LLMs are not designed to produce the normalized vector representations that AI Search requires for ANN indexing; their internal token representations are not suitable index vectors. CodeLlama-34B is a generative model specialized for programming code and does not produce the vector embeddings needed for a product catalog similarity index.
2. A Generative AI Engineer at Elmwood AI is building a document similarity search application using Databricks AI Search. The team requires cosine similarity ranking so results reflect semantic direction independent of vector magnitude. The engineer finds that Databricks AI Search uses L2 distance as its fixed underlying metric and that no distance_metric parameter exists on create_delta_sync_index(). What should the engineer do to achieve cosine similarity ranking within the existing infrastructure? (Select one!)
ASwitch to a Direct Access index, which supports configuring cosine distance as its underlying distance metric
BNormalize all document embedding vectors to unit length before inserting them into the Delta source table, and apply the same normalization to query vectors before calling similarity_search()
CApply normalization only to query vectors before calling similarity_search(), leaving stored document vectors at their original magnitudes
DSet rrf_param to 0 in the hybrid search configuration to disable BM25 and cause the system to rank purely by cosine similarity
Explanation
Databricks AI Search uses L2 Euclidean distance as its fixed metric, and the similarity score is computed as 1 divided by 1 plus the squared L2 distance. For unit-length vectors, the squared L2 distance equals 2 minus twice the cosine similarity, which means ranking by L2 distance and ranking by cosine similarity produce identical orderings when all vectors are normalized. The normalization must be applied consistently on both sides — document vectors at index time and query vectors at query time — for the equivalence to hold. Direct Access indexes use the same L2 distance metric as Delta Sync indexes; no API parameter exists to switch to cosine distance on either index type, making this approach ineffective. Normalizing only query vectors while leaving stored vectors at varying magnitudes breaks the mathematical equivalence, producing L2 rankings that diverge from true cosine rankings. The rrf_param controls Reciprocal Rank Fusion blending for hybrid search and has no effect on the underlying vector distance computation.
3. A Generative AI Engineer at Thornfield Manufacturing is building a RAG assistant that answers employee questions by drawing from two data sources: scanned maintenance PDF manuals and a live relational inventory database containing part numbers, stock counts, and reorder thresholds. Which two design decisions correctly handle each data source within the RAG pipeline? (Select two!)
Multiple correct answersAIndex the PDF manuals in AI Search using vector embeddings to support semantic similarity retrieval
BExport the inventory database to CSV files, convert rows to prose text chunks, and embed them in the same AI Search vector index as the PDFs
CQuery the inventory database directly through a SQL-based retrieval tool integrated into the RAG pipeline
DSummarize each database table as a paragraph, embed the summaries as vector chunks, and merge them into the PDF index
ERoute all employee queries through Feature Serving to retrieve pre-materialized part counts before sending them to the LLM
Explanation
PDF manuals are unstructured text, making vector embedding in AI Search the appropriate retrieval strategy. Semantic similarity search allows the pipeline to surface relevant manual passages even when the employee's phrasing differs from the document's exact wording. Inventory data is structured and relational, so querying it directly with SQL through a structured retrieval tool retrieves exact, current values — stock counts and reorder thresholds require precision and do not benefit from semantic approximation. Converting inventory rows to prose chunks and embedding them in a vector index is inappropriate for structured data: it degrades numeric precision and pollutes the semantic index with content that does not embed meaningfully. Table summaries as vector chunks share the same problem, collapsing exact relational values into approximate prose. Feature Serving exposes precomputed feature values at low latency and is not designed as a query interface for answering ad-hoc natural-language questions about inventory.
4. A Generative AI Engineer at Aldgate Systems is configuring a Feature Serving endpoint that supplies real-time context to a credit risk LLM application. The endpoint must retrieve a customer's pre-materialized account tier and recent transaction history from a feature table, and also compute a derived creditworthiness score on-demand by applying a business logic transformation to those retrieved values. Which two FeatureSpec components are required to implement both capabilities? (Select two!)
Multiple correct answersAFeatureLookup to retrieve pre-materialized columns from the feature table by primary key
BFeaturePipeline to orchestrate the lookup and transformation steps in sequence
CFeatureFunction to apply on-demand transformations and compute derived feature values at serving time
DFeatureGroup to bundle related feature columns into a single retrievable unit
EFeatureScheduler to trigger periodic recomputation of the creditworthiness score
Explanation
A FeatureSpec supports two component types that address the two distinct requirements here. FeatureLookup retrieves specific columns from a pre-materialized feature table using a primary key, which is exactly how account tier and transaction history stored in a feature table are accessed at inference time. FeatureFunction applies a Python function on-demand at serving time, producing derived values — such as a creditworthiness score computed from the looked-up inputs — that are calculated fresh for each request rather than stored in advance. FeaturePipeline is not a component of FeatureSpec; the coordination between lookups and functions is handled implicitly by the FeatureSpec definition itself. FeatureGroup is not a recognized FeatureSpec component; bundling related features is achieved by including multiple FeatureLookup entries within the same spec. FeatureScheduler does not exist as a FeatureSpec component; periodic recomputation of materialized features is managed through separate scheduled pipeline runs outside the serving endpoint.
5. A Generative AI Engineer at Holloway Finance is building a RAG application for personalized investment advice. At query time, the system must retrieve a pre-computed risk tolerance score for the user from an online feature store and also compute a real-time portfolio diversification index by applying a formula to several stored portfolio metrics. The engineer is designing the FeatureSpec configuration. Which two FeatureSpec component types should they include? (Select two!)
Multiple correct answersAFeatureLookup to retrieve the pre-computed risk tolerance score from the online feature store by user entity key
BFeatureSummary to compute aggregate statistics across the user's historical transaction records
CFeatureFunction to apply the portfolio diversification formula to the stored portfolio metrics at query time
DFeatureStream to subscribe to live market data updates during the serving request
EFeatureCache to store previously retrieved portfolio metrics for faster subsequent lookups
Explanation
FeatureLookup retrieves pre-materialized column values from an online feature store by entity key, making it the correct component for fetching the stored risk tolerance score for a given user. FeatureFunction applies a user-defined transformation or formula to derive on-demand features at serving time, making it the right tool for computing the portfolio diversification index from stored portfolio metrics during the request. FeatureSummary is not a valid component type in the Databricks Feature Serving API; aggregate statistics would be pre-materialized as features through the upstream engineering pipeline rather than computed inline within the FeatureSpec. FeatureStream is not a FeatureSpec component; real-time data delivery in Feature Serving is handled through the online store update pipeline, not a streaming subscription defined inside the FeatureSpec. FeatureCache is not a FeatureSpec component type; caching behavior within Feature Serving is managed by the endpoint infrastructure automatically and is not a configurable element of the FeatureSpec definition.
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