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Databricks · DB-GAIE · Associate

Databricks Generative AI Engineer Associate — Practice Questions and Mock Exam

The Databricks Certified Generative AI Engineer Associate exam validates the ability to design, build, and deploy generative AI applications on the Databricks platform.

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Checked against Databricks · September 2026 · Current exam version

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Assembling and Deploying Applications1 / 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 Applications2 / 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 Preparation3 / 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 Monitoring4 / 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

Application Development5 / 5

Which feature differentiates Mosaic AI Agent Framework from using standalone LangChain without Databricks integration?

AlexFull explanation from Alex

The Agent Framework's value proposition is bridging the gap between prototype and production. LangChain provides excellent building blocks for agent construction, but productionizing an agent requires deployment infrastructure, monitoring, quality evaluation, human feedback collection, and governance. The Databricks Agent Framework adds these production capabilities as integrated platform services rather than requiring teams to build and maintain them independently.

Sourcedocs.databricks.com

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Conversation preview · DB-GAIE

Current information for agents

  1. You

    How can an agent answer using information newer than its training?

  2. Alex · AI tutor

    Give it an appropriate search or real-time API tool to retrieve current information.

    Read the Databricks documentation ↗
  3. You

    Does the model automatically know the newest events?

  4. Alex · AI tutor

    No. The tool supplies fresh information for the agent to use in the answer.

    Read the Databricks documentation ↗

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DB-GAIE · Question excerpt

Current information for agents

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

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A tool-calling agent with a web search or real-time API tool

A tool-calling agent can retrieve current information from a search or real-time API tool.

Read the Databricks documentation ↗

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What you practice for DB-GAIE.

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Develop grounded AI applicationsSee the guide for weighting85 QuestionsPass-IT questions

Select models and tools, then improve responses with evidence.

  • Choose LangChain or similar tools, MLflow and Agent Framework, and connect multi-agent systems to Genie spaces or conversational APIs for data retrieval.
  • Select LLMs and embedding context lengths using application needs, document and query characteristics, model cards and experimental metrics.
  • Adapt prompts to the desired response and enrich them with the user’s fields, terms and intent.
  • Choose chunking from model and retrieval evaluations, inspect responses for quality and safety, and implement guardrails.
  • Distinguish evaluation during development from monitoring an application in operation.
Official Databricks objectives
Assemble and deploy AI systemsSee the guide for weighting59 QuestionsPass-IT questions

Connect application components and deliver them with controlled access.

  • Implement chains with pyfunc pre- and post-processing; configure RAG models, embeddings, retrievers, dependencies, examples and signatures, then register them in Unity Catalog with MLflow.
  • Create and query Mosaic AI Vector Search indexes, sizing them for embedding volume, update frequency, latency and cost.
  • Serve applications with Foundation Model APIs, use ai_query() for suitable batch inference and control resource access from serving endpoints.
  • Store intermediate memory persistently, integrate managed, external or custom MCP servers, and build an interactive interface suited to the agent’s users.
  • Version prompts and use CI/CD to update indexes, promote prompts across environments and test agent components.
Official Databricks objectives
Design the application flowSee the guide for weighting47 QuestionsPass-IT questions

Translate a business goal into explicit inputs, outputs and actions.

  • Define pipeline inputs and outputs, choose model tasks and chain components and write prompts that request the required response format.
  • Select and order knowledge-retrieval or action tools for multi-stage reasoning.
  • Choose when Agent Bricks Knowledge Assistant, Multiagent Supervisor or Information Extraction fits the problem.
Official Databricks objectives
Prepare data for retrievalSee the guide for weighting51 QuestionsPass-IT questions

Build a useful knowledge source for retrieval-augmented generation.

  • Select relevant, high-quality documents, extract their content with suitable Python packages and remove material that would degrade responses.
  • Choose document-aware and advanced chunking strategies within model constraints and write the chunks into Unity Catalog Delta tables in the right sequence.
  • Measure retrieval performance with appropriate tools and metrics and explain how reranking improves result ordering.
Official Databricks objectives
Evaluate and monitor AI behaviorSee the guide for weighting47 QuestionsPass-IT questions

Measure quality in development and watch deployed applications.

  • Choose model size and architecture using quantitative results, and select monitoring metrics for the deployment scenario.
  • Evaluate agents with MLflow scoring, tracing and custom scorers, distinguish judges that need ground truth and incorporate subject-matter feedback.
  • Use inference logging, inference tables and Agent Monitoring to assess deployed RAG applications and live endpoints.
  • Control LLM costs and track Agent Framework deployments with AI Gateway inference tables, usage tables and rate limits.
Official Databricks objectives
Apply governance and guardrailsSee the guide for weighting37 QuestionsPass-IT questions

Address unsafe input and source-data risks in application design.

  • Use masking as a guardrail while meeting performance needs and select protections against malicious user input.
  • Check source-data legal and licensing requirements when choosing what the application can use.
  • Choose an alternative treatment for problematic text entering the application from a data source.
Official Databricks objectives
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Frequently asked questions

Your preparation

How much study time should I plan for DB-GAIE?

For planning, Pass-IT estimates roughly 50–120 hours. Relevant experience may put you toward the lower end; starting from scratch may put you toward the higher end. Your needs may fall outside this range. This is not a time requirement from Databricks or a promise that you will be exam-ready within that time.

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.

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What language are Pass-IT questions in?

Questions in the app are currently in English. Translations of the sample questions on this page apply only to this preview. Check with Databricks for the languages available for the official exam.

How long does my DB-GAIE access last?

Access covers this certification for 12 months. You pay once, with no automatic renewal.

When does the money-back guarantee apply?

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days. Guarantee terms

About the DB-GAIE exam

Where can I take the DB-GAIE exam?

Online proctored or test center.

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.

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.

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Guarantee terms

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days.

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