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
Truncation is the mechanism: if max_tokens is too low, the summary is cut off before covering all key points. Raising it allows the model to emit a complete summary, directly addressing the missed key points.
- ✗
Increase the top_k parameter.
Why it's wrong here
top_k limits sampling to the k most probable tokens; increasing it broadens token selection and adds variability, which does not restore key points omitted from a long document. It is tempting because top_k is a standard Bedrock inference parameter, but it would be the correct choice when tuning output diversity rather than summary coverage.
- ✗
Increase the temperature parameter.
Why it's wrong here
Temperature scales sampling randomness, so raising it makes token selection less deterministic and increases variation, not coverage of source content. It suits creative or diverse generation. Missing key points stems from the model not attending to all input, which temperature does not change.
- ✗
Increase the top_p parameter.
Why it's wrong here
top_p (nucleus sampling) controls the cumulative probability mass of tokens considered; raising it widens token choice and increases randomness rather than recovering omitted source content. It is tempting because sampling parameters shape output, but top_p would be the correct adjustment when the goal is more varied or creative generation.
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