AIF-C01 Fundamentals of Generative AI Practice Question
A developer is using Amazon Bedrock's Claude model to summarize long documents. The developer notices that the summaries sometimes miss key points. Which parameter adjustment is most likely to improve summary completeness?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that parameters controlling randomness (temperature, top_k, top_p) affect output length or completeness, when in fact they only influence token selection diversity and creativity.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the max_tokens parameter.
Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the max_tokens parameter.
Why this is correct
More tokens allow the model to include more details in the summary.
- ✗
Increase the top_k parameter.
Why it's wrong here
Top_k limits the number of tokens considered, not length.
- ✗
Increase the temperature parameter.
Why it's wrong here
Higher temperature makes output more random, not more complete.
- ✗
Increase the top_p parameter.
Why it's wrong here
Top_p controls nucleus sampling, not output length.
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