Hands-On Skills for Databricks GenAI Engineer

The Databricks Generative AI Engineer Associate guide recommends practical experience because many objectives are easier to understand after you have built the pipeline yourself. Hands-on work should prove that you can connect data preparation, retrieval, agents, serving, evaluation, and governance—not merely click through a product tour.

This practice list is organized as small labs that can be repeated and inspected. Each lab should leave evidence: a table, trace, evaluation result, serving endpoint, indexed corpus, permission decision, or short design note explaining the trade-off.

Turn a business request into an AI pipeline

Choose a simple internal support or research use case and write the expected inputs, outputs, data sources, success criteria, and actions.

Decide whether it needs a prompt-only solution, RAG, a tool-calling agent, or a managed Agent Bricks pattern.

Create prompts with explicit output contracts

Build prompts that return a defined schema or tightly constrained format. Test missing fields, ambiguous inputs, and examples that violate the expected pattern.

Record which prompt change improved the behavior and which only made the prompt longer.

Prepare documents for retrieval

Use several file types and compare extraction quality. Remove headers, navigation, repeated disclaimers, or other noise that harms retrieval.

Store useful source metadata so chunks can be traced back to their document and governed correctly.

Experiment with chunk size and overlap

Create a small query set and compare retrieval when chunks are too small, too large, or heavily overlapping.

Measure result quality and record count together because chunking affects both search quality and index scale.

Write chunked content into governed tables

Store prepared text and metadata in Delta tables under Unity Catalog. Define ownership and permissions before exposing the data to the application.

This makes the link between data engineering and GenAI governance concrete.

Build a Vector Search index

Create an index over a controlled corpus, query it with representative questions, and inspect the returned metadata and rankings.

Add structured filters and compare semantic, hybrid, or reranked results where supported.

Create a basic RAG chain

Retrieve evidence, assemble the context, call a model, and preserve source references. Include one question that the corpus cannot answer.

Evaluate whether the system abstains or explains insufficient evidence rather than producing an unsupported response.

Build one tool-calling agent

Give the agent a narrow retrieval tool and one safe action or lookup tool. Define error results, state, and a finish condition.

The general tool-use principles help keep authorization and execution separate from model choice.

Try an Agent Bricks use case

Use a Knowledge Assistant, Information Extraction, or Supervisor pattern on a task that matches its intended strength.

Write down why the managed approach is appropriate and what requirement would push you toward a custom agent instead.

Integrate structured data through an agent path

Use a governed structured-data capability such as a function or supported conversational data interface to answer a question the document retriever cannot answer.

Keep the permission boundary visible so the agent does not gain broader access than the user or application should have.

Connect an MCP server in a controlled lab

Compare a managed MCP server with an external or custom server scenario. Focus on authentication, permissions, tool discovery, and operational ownership.

Do not expose a broad tool catalog when the agent needs only a few capabilities.

Trace the agent with MLflow

Capture inputs, outputs, intermediate steps, retrieval calls, tools, latency, tokens, and errors.

Use the trace to explain one failure without relying on the final response alone.

Build an evaluation dataset

Create representative normal, edge, and failure cases. Apply code-based checks where possible and LLM judges or custom scorers where nuanced quality needs assessment.

Compare two app versions and explain why one is better using evidence.

Collect subject-matter-expert feedback

Ask reviewers to score a small sample with a clear rubric. Compare disagreement and refine the rubric when ratings are inconsistent.

Human feedback should improve the evaluation set rather than remain unstructured comments.

Register and serve a model or application

Use MLflow and Unity Catalog where appropriate, create a serving endpoint, set permissions, and call the endpoint from a simple client.

Record latency and inspect how the endpoint scales or reports errors.

Enable inference and usage observability

Use the supported gateway, inference-table, tracing, or usage features for the endpoint or agent.

Confirm which data is logged and apply governance appropriate to the payloads.

Move one prompt through a release workflow

Version a prompt, validate it in a nonproduction environment, promote it after tests pass, and keep the previous version available for rollback.

This applies the same release logic described in CI/CD fundamentals to a GenAI artifact.

Practice model selection with a small evaluation matrix

Compare two candidate models on task success, latency, cost, context needs, and structured-output behavior using the same cases.

This turns model choice into measurable engineering rather than personal preference.

Build one multi-agent supervisor scenario

Create or diagram specialist agents with non-overlapping responsibilities and a supervisor that delegates and synthesizes results.

Record which data and tools each subagent can access so orchestration and permission boundaries remain explicit.

Use Information Extraction on an unstructured source

Define a schema, process a small set of documents, and inspect where extraction fails or produces ambiguous fields.

Then decide which validation or human-review rule is needed before the structured output feeds another system.

Compare managed and custom tools

Use a managed Databricks capability for one task and a custom function or external MCP service for another.

Write down the difference in maintenance, authentication, permissions, observability, and deployment ownership.

Test one serving failure

Simulate a timeout, permission denial, or rate limit in a controlled environment and observe how the client handles it.

Production readiness includes a clear failure response, not only a successful demo request.

Use traces to debug a bad RAG answer

Follow the request from user input through retrieval, prompt assembly, model response, and any tool steps.

Identify whether the problem originated in source data, search, generation, or application logic before changing the prompt.

Practice a prompt rollback

Introduce a prompt version that improves one case but regresses another, then restore the previous approved version.

This makes version control and gated promotion tangible rather than abstract CI/CD terminology.

Practice query-to-evidence debugging

Take one bad answer and inspect the user query, retrieved chunks, ranking, prompt, model response, and trace.

Write down the first stage where the evidence became wrong. This trains the troubleshooting skill behind several exam sections at once.

Use Unity Catalog permissions in the labs

Create at least two roles or principals with different access and verify that the application or agent cannot retrieve or call assets outside its assigned scope.

Governance is easier to learn when permission denial is an observed behavior rather than a diagram.

Compare online and batch inference

Run a small interactive workload and an offline set of many records and decide which serving pattern better fits each one.

Record the differences in latency expectation, throughput, failure handling, and result correlation.

Evaluate an agent trajectory, not just its answer

Inspect whether the agent selected the right tools, made unnecessary calls, respected stop conditions, and recovered from errors.

A correct final answer can still hide an inefficient or unsafe path.

Build one user-facing interface

Create a simple Databricks App or equivalent controlled interface that calls the backend without exposing privileged credentials in the browser.

Use the lab to verify authentication, endpoint permissions, and user-context handling end to end.

Practice cost-aware architecture choices

Compare a larger model, reranking step, long context, and multi-agent workflow against a simpler baseline. Record when the extra quality justifies the additional latency and cost.

This lab reinforces that production GenAI engineering balances performance, quality, governance, and economics.

Use one governance failure as a lab case

Intentionally configure a test principal without access to one governed dataset or tool and confirm the application fails safely.

Then grant the minimum required permission and rerun the case. Observing denial and recovery makes Unity Catalog permissions more memorable.

Keep a troubleshooting notebook

For each failed lab, record the symptom, layer, evidence, fix, and rule you learned. Separate retrieval failures from serving failures, permission failures, and model-quality failures.

That notebook becomes a high-value final review because it contains decisions you personally had to debug.

Practice one licensing and source-quality decision

Choose a document source that is technically useful but has unclear licensing or ownership, then design the correct response: obtain permission, choose another source, or restrict use according to policy.

This reinforces that governance can change the architecture even when retrieval quality is excellent.

Run a small production-monitoring simulation

Take several stored traces, score a sample with the same criteria used during development, and inspect whether one failure pattern is increasing.

The exercise connects offline evaluation to ongoing operations instead of treating monitoring as a separate dashboard topic.

Finish with a capstone

Build a small application that ingests source data, retrieves evidence, uses an agent or chain, exposes a serving interface, records traces, evaluates quality, and applies permissions.

Then deliberately break one dependency and diagnose it. The troubleshooting step is where hands-on knowledge becomes exam-ready reasoning.