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LangChain for LLM Applications and RAG

Module 09 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. Streaming improves perceived responsiveness but must not…

2. Callbacks are primarily useful for…

3. When streaming a response that later fails validation, the app should…

4. Token usage captured via callbacks is valuable because…

5. Streaming tool-calling agents is harder because…

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Answer Key and Explanations

Check these only after attempting every question.

1. B - Present unverified partial output as a final, checked answer

Show progress while making clear what is provisional versus confirmed.

2. B - Observing execution - timing, token usage, tool calls, and errors

They are the instrumentation hook that makes behaviour visible.

3. B - Clearly replace or mark it, so the user does not act on invalid output

Streamed text is already on screen; failure must be communicated, not quietly dropped.

4. B - It makes cost per request measurable and attributable before it becomes a surprise

Cost problems are much cheaper to fix when they are visible per feature.

5. B - Intermediate steps are not final answers, so raw streaming can confuse users

Users need step-level framing ('searching…', 'drafting…'), not a raw thought stream.

Practical Check

Apply this module to your own work: complete the module activity for *Streaming and Callbacks*, 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.