EnglishDeutschFrançaisEspañolPortuguês

Databricks · DB-GAIE · Associate

Databricks Generative AI Engineer Associate — Practice Questions and Mock Exam

Practice with realistic DB-GAIE questions aligned to the exam objectives. Alex explains every answer, and your readiness score shows what to study next.

Start practicing DB-GAIE freeBuy DB-GAIE access for $29.99
Buy DB-GAIE access for $29.99Start practicing DB-GAIE free

One payment. Lifetime access to this certification.

Try 5 questions first →
45Questions
90minTime Limit

Checked against Databricks · August 2026Current exam version

Overview

About the exam

The Databricks Certified Generative AI Engineer Associate exam validates the ability to design, build, and deploy generative AI applications on the Databricks platform. It covers application design, data preparation and chunking, assembling and deploying retrieval-augmented generation pipelines, evaluation and monitoring, and governance. Candidates must demonstrate proficiency in prompt engineering, model-serving endpoints, and Databricks' generative AI tooling.

This certification is designed for AI engineers and developers with six or more months of experience building generative AI applications on the Databricks platform. It demonstrates the ability to take a generative AI application from design through production deployment.

Exam domains

What's on the exam

Application development is the dominant domain at 30%, nearly a third of the exam, covering framework choices like LangChain, prompt engineering, LLM guardrails, and model selection from a hub or marketplace. Assembling and deploying applications follows at 22% — coding chains as pyfunc models, registering them to Unity Catalog, and standing up Vector Search indexes for retrieval.

Design applications and data preparation each carry 14%, testing prompt design against business requirements and chunking strategy against document structure. Evaluation and monitoring (12%) and governance (8%) round out the exam, covering MLflow scoring, AI Gateway tracking, and guardrails against malicious input or licensing risk in source data.

Exam blueprint: DB-GAIE

Application Development30%

Assemble a GenAI application from framework choices like LangChain, a chunking and embedding strategy tuned to the source documents, and prompts that get augmented with user context, then guard against bad outputs, pick models using metrics and model cards, and track the whole system with MLflow and the Agent Framework.

≈ 24 h
Assembling and Deploying Applications22%

Code and deploy chains using pyfunc models with pre- and post-processing, assemble RAG applications with the appropriate embedding model, retriever, and Vector Search index, and register models to Unity Catalog with MLflow. Also covers applying CI/CD best practices, integrating MCP servers, and building user-facing interfaces for agent applications.

≈ 18 h
Design Applications14%

Craft prompts that produce a specific output shape, pick the model tasks and chain components a business requirement calls for, translate that requirement into concrete pipeline inputs and outputs, and decide when Agent Bricks like Knowledge Assistant or Multiagent Supervisor are the right building block.

≈ 11 h
Data Preparation14%

Chunk and clean source documents so only the useful content feeds a RAG pipeline, land that text in Delta tables under Unity Catalog, and judge retrieval quality well enough to know when re-ranking would help.

≈ 11 h
Evaluation and Monitoring12%

Choose an LLM and the metrics worth tracking for a given deployment, score and trace agent or RAG performance in MLflow, watch a live endpoint through AI Gateway and Agent Monitoring, and fold subject-matter feedback back into the system to keep improving it.

≈ 10 h
Governance8%

Mask sensitive fields to hit performance targets, guard the application against malicious user input, and check data sources against legal and licensing risk before problematic content ever reaches the model.

≈ 6 h

Format

Exam format and question types

The exam consists of 45 scored questions, a mix of multiple-choice and multiple-select formats, inside a 90-minute window; unscored pilot items may also appear. Questions run scenario-heavy: picking a chunking approach that fits a document set, matching chain components to a target input and output shape, or configuring Vector Search for a given latency and cost budget.

Question types: DB-GAIE

Multiple Choice100%

Pick the single best answer from four or five options — the exam's bread and butter.

Databricks confirms these question types — a percentage split is not published; the shares reflect our exam-aligned question pool.

Try it now

Try five DB-GAIE questions

Five questions straight from our Databricks Certified Generative AI Engineer Associate pool. Answer one — Alex explains the why.

Governance1 / 5

What is the relationship between a Databricks workspace and Unity Catalog metastore in the context of GenAI governance?

AlexFull explanation from Alex

In Unity Catalog, a metastore is the top-level container holding the catalog → schema → asset hierarchy with permissions. Multiple workspaces in the same region can share a single metastore, enabling cross-workspace governance of models, tables, and other assets. This means a model registered in dev is immediately visible in production with consistent access controls—no copying required. Distractor analysis: The option “A metastore can attach to at most one workspace at a time…” is wrong because metastores attach to many workspaces, not one. The option “Each workspace requires a dedicated metastore…” is wrong because shared metastores eliminate the need for per-environment metastores. The option “Unity Catalog assigns one metastore per cloud region…” is wrong because Unity Catalog assigns one metastore per region but workspaces within that region do share governance. Ref: docs.databricks.com/en/data-governance/unity-catalog/metastores.html

Sourcedocs.databricks.com

Assembling and Deploying Applications2 / 5

A team wants to A/B test two different prompt strategies for their customer support agent. How should they set this up using Databricks serving?

AlexFull explanation from Alex

A/B testing GenAI agents on Databricks uses the traffic_config in a serving endpoint to split requests between served entities. Each agent version is deployed as a separate served_entity on the same endpoint, with traffic_percentage controlling the split (must sum to 100). Each version's responses are logged separately in the inference table, enabling comparison of quality, latency, and user feedback. Distractor analysis: The option “Use Feature Serving to tag each request and route…” is wrong—Feature Serving is for feature lookup, not prompt routing. The option “Deploy a single endpoint with a pre-processing hook…” is wrong—pre-processing hooks don't provide proper traffic management or independent logging. The option “Register each prompt version in MLflow Model Registry…” is wrong—custom routing notebooks bypass Databricks' built-in traffic management and inference logging. Ref: docs.databricks.com/en/machine-learning/model-serving/serve-multiple-models-to-serving-endpoint.html

Sourcedocs.databricks.com

Design Applications3 / 5

Which approach allows a GenAI agent to access the most recent data when answering questions about current events?

AlexFull explanation from Alex

LLM knowledge is frozen at training time, so it cannot answer questions about current events. A RAG pipeline with Delta Live Tables (the option “A retrieval agent querying a continuously refreshed Delta…”) only retrieves indexed documents—not live web data. Expanding the context window with cached snapshots (the option “Expanding the context window to include a cached snapshot of…”) still uses stale data. Weekly fine-tuning (the option “Fine-tuning the base model weekly…”) introduces a multi-day lag. A tool-calling agent with web search or real-time API tools (the option “A tool-calling agent with a web search or real-time API tool”) invokes external data sources on demand, returning up-to-the-minute results. Databricks supports this via MCP servers, custom tools, and built-in web search integration with Gemini/OpenAI/Anthropic models. Ref: docs.databricks.com/en/machine-learning/model-serving/web-search; docs.databricks.com/en/generative-ai/agent-framework/agent-tool.

Sourcedocs.databricks.com

Data Preparation4 / 5

When creating a Delta Sync vector search index, what parameter specifies the column containing text to be automatically embedded?

AlexFull explanation from Alex

When creating a Delta Sync vector search index with managed embeddings, the embedding_source_column parameter identifies the text column Databricks will automatically embed. The platform calls the specified embedding model endpoint, batches the text, and stores resulting vectors in the index. The options “auto_embedding_column”, “vector_embedding_source”, and “text_vectorization_col” are fabricated parameter names—none appear in the Databricks SDK or REST API. For self-managed embeddings, you instead specify an embedding_vector_column containing pre-computed vectors. Ref: docs.databricks.com/en/generative-ai/vector-search/create-index; docs.databricks.com/en/vector-search/query-vector-search.

Sourceapi-docs.databricks.com

Evaluation and Monitoring5 / 5

Which Databricks feature allows engineers to trace the complete execution flow of a GenAI agent, including tool calls, retrieval steps, and LLM interactions?

AlexFull explanation from Alex

MLflow Tracing instruments GenAI applications automatically when using supported frameworks (LangChain, OpenAI, Databricks SDK). Each request creates a trace containing hierarchical spans that represent individual operations. Engineers can view traces in the MLflow UI to debug issues, identify bottlenecks (which tool call is slow?), verify retrieval quality (what did the retriever return?), and understand agent reasoning (what did the LLM decide and why?).

Sourcedocs.databricks.com

Question pool

326 questions, built like the exam

Every domain of the DB-GAIE exam has enough questions in the pool to practice it in depth. A mock exam asks 45 questions in one sitting, on the same 90-minute clock as the real thing.

Audit record: DB-GAIE

Spec check against DatabricksAugust 4, 2026

last verified against the official Databricks source

Blueprint coverage56 official objectives

across 6 domains, from the official exam guide

Pool size326 questions

= 7 full practice exams of 45 questions each — never the same question twice

Domain coverageall 6 domains at official weight

Application Development 85 · Assembling and Deploying Applications 59 · Design Applications 47 · Data Preparation 51 · Evaluation and Monitoring 47 · Governance 37

Canonically validated326 of 326

each verified against official Databricks documentation — answer, options and explanation, source cited

Methodology openly documented.How questions are made →

Planning

Preparing for DB-GAIE

How long you'll need depends on how much hands-on experience you bring. The rest is set by the vendor: how the exam is delivered, how soon you can retake it, and how long the credential stays valid.

The exam is delivered online with a remote proctor or at a test center, offered in English, Japanese, Portuguese (BR), and Korean. The credential holds for two years, and recertifying means passing the current version of the exam.

Your plan: DB-GAIE

Preparation

Study time50–120 h

typically around 50 h if you already work with this stack, around 120 h coming to it fresh

LevelAssociate
Worth having firstNone required. 6+ months hands-on GenAI development on Databricks recommended.

Exam day & after

DeliveryOnline proctored or test center
Retake policyNo mandatory waiting period. Retake fee applies.
Stays valid2 years

Recertification required every 2 years by taking the current exam version.

Official guideView on vendor site

The hours are our own planning estimate — Databricks publishes no preparation time for this exam. A starting point for your calendar, not a target.

Watch out

Common pitfalls

Vector Search configuration is where GenAI theory meets Databricks specifics: the exam expects fluency with embedding dimensions, index refresh modes, and the latency and cost trade-offs across configurations rather than conceptual RAG knowledge alone. Chunking strategy questions test judgment against a specific document structure rather than a memorized rule, and the same logic applies to picking an LLM — model cards and experiment metrics matter more than brand familiarity. Governance is only 8% of the exam but tests specific, detailed scenarios around guardrails and licensing risk that general AI-safety knowledge doesn't cover.

Watch list: DB-GAIE

  1. 01Agent Bricks Selection

    Building a custom multi-agent chain from scratch instead of recognizing when a prebuilt Agent Bricks template for grounded Q&A, multi-agent routing, or structured extraction already solves the scenario leads to wrong answers on application-design questions.

  2. 02MCP Server Integration

    Not distinguishing a managed MCP connector from an externally hosted or fully custom MCP server for a given application requirement leads to wrong answers on tool-and-agent assembly questions.

  3. 03Re-ranking Role

    Skipping re-ranking as a distinct retrieval step, instead of treating the top vector-search results as final, leads to wrong answers on retrieval-pipeline design questions.

  4. 04AI Gateway Tracking

    Confusing AI Gateway's inference and usage tracking with basic inference-table logging, or forgetting rate limiting as a cost control, leads to wrong answers on LLM-deployment monitoring questions.

  5. 05Licensing Risk

    Overlooking data-source licensing terms when selecting training or retrieval content leads to wrong answers on governance questions about legal risk in a GenAI pipeline.

Pass-IT trains you on exactly these weak spots — adaptive & spaced →

Questions

Frequently asked questions

How long is the Databricks Certified Generative AI Engineer Associate exam?

The Databricks Certified Generative AI Engineer Associate exam has 45 questions and a 90-minute time limit.

What are common mistakes on the Databricks Certified Generative AI Engineer Associate exam?

Common pitfalls include: Agent Bricks Selection, MCP Server Integration, Re-ranking Role, AI Gateway Tracking, Licensing Risk. Focus study time on these areas to avoid losing points.

Which Generative AI Engineer Associate topics carry the most weight?

Application development is the largest area at 30%, followed by assembling and deploying applications at 22%. Design and data preparation take 14% each, evaluation and monitoring 12% and governance 8%. More than half the exam is therefore building and shipping a generative application rather than reasoning about models.

What do you need before the Generative AI Engineer Associate exam?

No certification is required. Databricks recommends six or more months of hands-on generative AI development on the platform, and the catalog budget is about 80 hours. The exam is written from the builder side, so retrieval, chaining and deployment questions assume you have shipped something.

How long does the Generative AI Engineer Associate last?

Two years, after which you sit the current version of the exam again. Databricks offers no continuing-education alternative. On a subject moving this fast, the resit is likely to be a materially different exam.

Generative AI Engineer or Machine Learning Associate?

The machine learning exam is about training, tracking and deploying your own models, with Databricks Machine Learning itself at 38% of it. The generative AI exam assumes the model already exists and tests what you build around it: retrieval, chaining, evaluation and governance. Take the one that matches whether you train models or wire them up.

Pass-IT is an independent study tool, not affiliated with or endorsed by Databricks; Databricks and exam names are trademarks of their respective owners.

One certification. One payment.

Full DB-GAIE access

Get the full question pool for this certification. Alex explains every answer, and your readiness score shows what to work on next.

Buy DB-GAIE access for $29.99One payment. Lifetime access to this certification.
Take the free readiness check20 questions. No card. See what to study before you buy.

Reach 80% readiness and pass — or your money back.

How the score works →

Related certifications

Databricks Certified Associate Developer for Apache SparkDatabricks Certified Data Analyst AssociateDatabricks Certified Data Engineer AssociateDatabricks Certified Data Engineer ProfessionalAll Databricks certifications →