AIF-C01 Applications of Foundation Models Practice Question
A company uses Amazon Bedrock to generate marketing copy. The summaries are too verbose. Which parameter should be decreased to directly limit the length of the output?
⚠ Common exam trap
It's easy for candidates to confuse parameters that affect output style (temperature, top_p, frequency_penalty) with the one that directly controls output length (max_tokens), leading them to pick a parameter that changes how the model writes rather than how much it writes.
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
✓
max_tokens
The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) in the generated output. By decreasing this value, you explicitly cap the length of the marketing copy, making it less verbose. This is the most direct way to limit output length in Amazon Bedrock and other LLM APIs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
max_tokens
Why this is correct
max_tokens sets the maximum number of tokens in the response.
- ✗
temperature
Why it's wrong here
Temperature controls randomness, not output length.
- ✗
top_p
Why it's wrong here
top_p affects nucleus sampling, not length.
- ✗
frequency_penalty
Why it's wrong here
Frequency penalty discourages repetition, but does not directly limit length.
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