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AI Engineering Skills Assessment

Sixteen short questions in eight areas, from test automation to AI evaluation. You'll see your starting point in each area and what to study first, including where you'd start in the Automation to AI Engineering program.

This is a self-check to guide your learning. It doesn't certify your skills or job readiness, and your answers stay in your browser.

1. Test automation

A Playwright test clicks a button before it has finished rendering, and fails now and then. What is the best fix?

What is a page object for?

2. Programming (Python)

In Python, what does [x * 2 for x in range(3)] produce?

What does a pytest fixture do?

3. APIs and data

An API returns 201 after a POST. What does that usually mean?

Why validate an API response against a JSON schema in a test?

4. LLM fundamentals

What is a token, for a language model?

You need the model's answer as JSON your code can parse. What is the most reliable approach?

5. Retrieval (RAG)

What does retrieval-augmented generation (RAG) add to a model call?

A RAG answer sounds right but cites nothing from the documents. What should a test check?

6. Agents and tools

What makes an AI system an agent rather than a single model call?

An agent can issue refunds through a tool. Which safeguard matters most?

7. AI evaluation

Why can't you test an LLM feature only with exact string matches?

A new prompt version scores lower on your golden set of questions. What should the release pipeline do?

8. Cloud and delivery

Where should an application's API keys live in production?

What does a CI pipeline typically do on every pull request?

0 of 16 answered. Unanswered questions count as "don't know yet".
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