hardMultiple Choice
AIF-C01 Practice Question: A data scientist is using Amazon Bedrock to…
A data scientist is using Amazon Bedrock to generate product descriptions. They notice the output is often repetitive and lacks creativity. Which combination of parameter adjustments is MOST likely to produce more diverse and less repetitive output?
⚠ Common exam trap
AWS often tests the misconception that increasing temperature alone is sufficient for diversity, but candidates forget that top-p must also be increased to avoid the model repeatedly sampling from a narrow set of high-probability tokens.
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 temperature and increase top-p
Increasing temperature raises the probability of sampling lower-probability tokens, which increases randomness and diversity. Increasing top-p (nucleus sampling) expands the set of tokens considered for sampling, further reducing repetitiveness. Together, these adjustments encourage the model to explore a wider range of possible continuations, producing more creative and less repetitive output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease temperature and decrease top-p
Why it's wrong here
Lowering temperature sharpens the probability distribution and lowering top-p truncates it to fewer tokens, both pushing output toward the highest-probability, most repetitive phrasing. It is tempting because these settings reduce hallucination, and they would be correct for factual, deterministic responses.
- ✗
Decrease temperature and increase top-p
Why it's wrong here
Lowering temperature alone narrows sampling toward likely tokens, so repetition persists regardless of raising top-p, which merely widens the candidate pool. It is tempting because the two parameters are often tuned together, and it would suit tasks needing mild variation with high factual reliability.
- ✗
Increase temperature and decrease top-p
Why it's wrong here
Raising temperature increases randomness, but lowering top-p narrows the sampling pool to fewer tokens, cancelling that diversity and often worsening repetition. Top-p is intended to truncate improbable tokens for coherence, so it suits factual or deterministic tasks where you want controlled, focused output.
- ✓
Increase temperature and increase top-p
Why this is correct
Temperature scales the sampling distribution's randomness, while top-p restricts sampling to the smallest token set whose cumulative probability exceeds p. Raising both widens the candidate pool and flattens selection, producing more diverse, less repetitive product descriptions.
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