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LLMOps for Reliable AI Applications

Module 04 Knowledge Check

5 questions. Pass mark 4 out of 5. Answer every question before checking the answer key below, then retry after reading the feedback.

1. Which part of an LLM app can be tested fully deterministically?

2. A prompt change should be accompanied by…

3. Testing tool functions independently of the model matters because…

4. A test that asserts an exact model output string will…

5. Mocking the model in unit tests is appropriate when…

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Answer Key and Explanations

Check these only after attempting every question.

1. B - Schema validation, tool argument handling, and retrieval filters

Deterministic surfaces deserve ordinary unit tests; save statistical evaluation for the model's behaviour.

2. B - Re-running the eval set to detect regressions elsewhere

Prompts are code. An untested prompt edit is an untested deploy.

3. B - It separates 'the tool is broken' from 'the model called it wrongly'

Two very different fixes; you need to know which one you are looking at.

4. B - Be flaky and get disabled, removing coverage

Brittle assertions erode trust in the suite, which is worse than having no assertion.

5. B - You are testing the surrounding logic - parsing, routing, error handling - not the model

Mock to isolate your code; use real calls in evaluation to measure behaviour.

Practical Check

Apply this module to your own work: complete the module activity for *Unit Tests for Prompts, Schemas, and Tools*, then write one sentence naming what your result shows and one naming what it does not.

Strong Answer Pattern

A strong answer names the task, the evidence used, the check performed, and the remaining limitation. It avoids "proved", "guaranteed", or "always" unless the evidence genuinely supports it.