Free Databricks GenAI Engineer practice questions 2026
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Free Databricks GenAI Engineer practice questions 2026: 20 questions without signup

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6 domains 967 questions 90 min exam
Questions on the exam
45
Time allowed
90 minutes format →
Exam fee
$200 — vendor, checked October 6, 2026 detail →

967 practice questions for Databricks Certified Generative AI Engineer Associate, grouped by exam domain. Every question below shows all four options, which one is correct, and why each of the other three is not — the wrong answers are where most candidates lose marks.

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Questions by domain

Sample questions

Track the artifact: Which implementation meets the requirement?

Application Development Easy
A document-processing pipeline works in a notebook but fails when served because dependencies and input expectations are unclear. The exhibit requires an artifact, dependency specification, model signature, input example, and run trace. Which implementation meets the requirement?
  1. Track the artifact, dependencies, signature, example, and trace. ✓
    This records every evidence type required by the exhibit for reproducibility and serving validation.
  2. Save notebook output and describe inputs in a project note.
    An informal note does not provide the required tracked dependencies, signature, input example, and run trace.
  3. Log only the final artifact.
    An artifact alone omits dependencies, interface expectations, and execution evidence.
  4. Save the artifact and dependency file, then rerun the notebook without tracking the signature, input example, or execution trace.
    This omits three required evidence types even though it preserves the artifact and dependencies.
The trap
Treats output as complete lifecycle evidence. Replaces tracked metadata with prose. Provides only partial reproducibility evidence.

All 292 Application Development questions →

Use on-behalf-of auth with needed scopes and grants: Which implementation meets the requirement?

Assembling and Deploying Applications Hard
A Databricks Model Serving endpoint can be called, but every employee must see only data allowed for that employee. The current shared service principal queries the data successfully but ignores individual entitlements. Which implementation meets the requirement?
  1. Tell the model to filter records according to each employee's identity.
    Prompt instructions cannot enforce table permissions or turn a shared service identity into the employee's identity.
  2. Give the shared service principal broader permissions on every table.
    Broader permissions for one shared identity still cannot apply each employee's individual entitlements and increases exposure.
  3. Use on-behalf-of auth with needed scopes and grants. ✓
    Supported on-behalf-of authentication carries the caller identity for downstream authorization when the required scopes and grants are configured.
  4. Grant every employee permission to invoke the endpoint.
    Endpoint invocation permission controls who may call the endpoint, not which downstream records each employee may access.
The trap
Confuses endpoint access with data authorization. More access for one shared identity does not create caller-specific authorization. Prompts are not access-control mechanisms.

All 212 Assembling and Deploying Applications questions →

Authenticate first: Which implementation best meets the requirements?

Design Applications Medium
An internal policy assistant must answer from current approved documents, enforce user access before searching, return citations, and reject malformed structured output. The team can use a fixed pipeline. Exhibit: identity check, retriever, prompt assembly, model, parser, response. Which implementation best meets the requirements?
  1. Retrieve approved documents, generate an answer without using them as context, then authenticate and attach citations after generation.
    Authentication must precede retrieval, and the model must use retrieved context during synthesis; attaching citations afterward does not satisfy those requirements.
  2. Authenticate, retrieve, and answer from the documents.
    This omits prompt assembly, structured-output parsing, and validation, so malformed responses may be returned.
  3. Authenticate first, retrieve permitted documents, assemble the prompt, generate, parse, validate, and return citations. ✓
    This sequence checks identity before protected retrieval, grounds generation in approved documents, and validates the structured response before returning citations.
  4. Authenticate, retrieve permitted documents, assemble the prompt, generate, return citations, and validate the response afterward.
    Validation occurs after the response is returned, so malformed structured output can already reach the consumer.
The trap
It leaves required output checks unspecified. The validation stage is too late. It performs protected retrieval before authorization and generates without grounded context.

All 136 Design Applications questions →

Retrieve child chunks and their parent sections: Which design best meets both needs?

Data Preparation Medium
A retail catalog contains lengthy product sections with specifications and exceptions. An assistant must find exact warranty facts while retaining enough surrounding information to interpret them. Which design best meets both needs?
  1. Copy each parent section into every child chunk before indexing.
    Copying parent text inflates indexed content and does not cleanly separate precise retrieval from contextual presentation.
  2. Index complete product sections and return matching sections.
    Complete-section retrieval preserves context but can reduce precision when a small warranty detail is buried in a long section.
  3. Retrieve child chunks and their parent sections. ✓
    Child chunks support precise matching, while parent sections provide the surrounding context needed for interpretation.
  4. Create equal-sized chunks without retaining document hierarchy.
    Equal-sized chunks can split related details and provide no way to restore the surrounding product context.
The trap
Optimizes context without precise matching Uses duplication instead of parent context Discards document hierarchy

All 133 Data Preparation questions →

Configure an inference table and use tracing for latency: Which implementation best meets the requirement?

Evaluation and Monitoring Hard
An operations agent runs on a live serving endpoint. The team must retain request and response content plus serving metadata under Unity Catalog for later audits. MLflow traces may diagnose nested retrieval latency, but audit retention is the priority and delayed availability is acceptable. Which implementation best meets the requirement?
  1. Capture token and cost usage records only.
    Usage records support consumption analysis but do not retain complete request and response content for audits.
  2. Use MLflow traces as the sole governed audit store for endpoint traffic.
    Tracing is useful for operation diagnosis but does not replace the required configured inference records under Unity Catalog.
  3. Configure an inference table and use tracing for latency diagnosis. ✓
    An inference table addresses governed endpoint records, while tracing separately exposes nested operation timing.
  4. Write request and response logs to an unmanaged local application file.
    A local file does not satisfy the requirement for governed records under Unity Catalog.
The trap
Confuses usage accounting with content auditing. Substitutes diagnostic telemetry for audit storage. Misses the required storage boundary.

All 116 Evaluation and Monitoring questions →

Redact unnecessary identifiers before external: What is the next required control?

Governance Medium
Before sending research-assistant prompts to an external model, the team has inventoried sensitive fields, reviewed access, configured retention, and tested answer quality. Some prompts still contain unnecessary customer identifiers. What is the next required control?
  1. Increase retrieval depth before sending the existing prompt externally.
    Additional retrieval does not address unnecessary identifiers already present in the outbound prompt.
  2. Redact unnecessary identifiers before external transmission. ✓
    Removing unnecessary identifiers before the external call reduces exposure while preserving needed prompt content.
  3. Replace the external model with a larger model to reduce exposure.
    Model size does not remove sensitive fields from the outbound prompt or satisfy data minimization.
  4. Disable provenance records for prompts sent to the external model.
    Disabling records does not remove sensitive data from transmission and may weaken governance.
The trap
Model capacity is unrelated to redaction. More context does not reduce transmitted sensitivity. Less logging does not change outbound prompt content.

All 78 Governance questions →

Map `query` to `question`: What change resolves this specific defect?

Application Development Medium
A research assistant uses the template `Answer {question} using {context}`. Its caller supplies `query` and `context`; execution fails before model invocation. What change resolves this specific defect?
  1. Add more retrieval documents first.
    More documents cannot fix a template variable mismatch that fails before model invocation.
  2. Replace the model with a larger one.
    Model size is unrelated to the unresolved `question` variable.
  3. Move context into the system prompt and remove retrieval.
    This changes the application and discards retrieval context without fixing the missing `question` variable.
  4. Map `query` to `question`. ✓
    The template requires `question`, while the caller provides `query`; mapping the names fixes the pre-invocation failure.
The trap
Changes retrieval instead of the failing interface. Mistakes an orchestration error for a model-capacity issue. Makes an unnecessary architecture change.

All 292 Application Development questions →

The identity lacks required MCP service authorization: What is the most likely diagnosis?

Assembling and Deploying Applications Medium
An incident-triage agent calls a Databricks-managed MCP service. The service is supported and reachable, but the trace shows a 403 response. The agent identity can invoke its model endpoint; administrators have not granted the identity EXECUTE or namespace access to the MCP service. What is the most likely diagnosis?
  1. The identity lacks required MCP service authorization. ✓
    The evidence directly indicates missing EXECUTE and namespace grants for the managed MCP service.
  2. The model needs additional fine-tuning for MCP.
    A 403 with missing MCP grants indicates authorization failure, not insufficient model training.
  3. The endpoint invocation permission should automatically authorize every MCP call.
    Endpoint invocation permission does not grant access to downstream MCP services or their governed resources.
  4. The managed server must be replaced with a custom MCP App.
    A supported managed service remains suitable; missing authorization does not require rebuilding its hosting.
The trap
Endpoint access and MCP authorization are separate controls. Protocol authorization errors are not model-quality symptoms. Replacing hosting does not supply the missing grants.

All 212 Assembling and Deploying Applications questions →

Databricks GenAI Engineer exam: the facts

How many questions are on the Databricks GenAI Engineer exam?

45, as published by the exam vendor.

How long is the Databricks GenAI Engineer exam?

90 minutes. Across 45 questions that is about 120 seconds per question.

What topics does the Databricks GenAI Engineer exam cover?

6 domains: Application Development, Assembling and Deploying Applications, Design Applications, Data Preparation, Evaluation and Monitoring, Governance. Weights: Application Development 30%, Assembling and Deploying Applications 22%, Design Applications 14%, Data Preparation 14%, Evaluation and Monitoring 12%, Governance 8%.

How many Databricks GenAI Engineer practice questions does Certsqill have?

967, spread across 6 exam domains. Every one shows all options, which is correct, and why each of the others is not.

Why Databricks Certified Generative AI Engineer Associate dumps are risky

Databricks Certified Generative AI Engineer Associate dumps claim to be real exam questions. Even apart from exam rules, they are poor preparation: you cannot check whether the answers are right, and Databricks’ exam guide states that it covers the version live as of 18 March 2026 — so anything collected before that may describe tools and objectives that have since changed.

This exam moves with the platform: Agent Bricks, Vector Search, MLflow evaluation, AI Gateway and guardrails all appear in the current guide. Practice questions written against that guide, with an explanation for every option, keep you on the current version.

Databricks GenAI Engineer practice: questions people ask

Do these questions follow the current Databricks exam guide?

Yes. They are written against the six sections and weights of the March 2026 exam guide, and your readiness score is shown per section.

Would you pass Databricks GenAI Engineer today?

Five minutes, and you get a score per domain — not one number, but which section to open tonight.

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Certsqill Databricks GenAI Engineer question bank · 967 questions across 6 domains · Every answer, right and wrong, comes with its own explanation.