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AIF-C01 Practice Question: A developer is using Amazon Bedrock to generate…
A developer is using Amazon Bedrock to generate text responses. They want to reduce the randomness of the output and make the model more deterministic. Which parameter should the developer decrease?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the distinction between temperature (direct logit scaling) and top_p (cumulative probability cutoff), leading candidates to incorrectly choose top_p when the question asks for reducing randomness, because both affect diversity but temperature is the more direct and commonly used parameter for determinism.
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
✓
Temperature
Temperature directly controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) reduces the probability of sampling less likely tokens, making the model more deterministic and focused on the highest-probability next token. This is the standard parameter for adjusting output creativity versus determinism in large language models like those on Amazon Bedrock.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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stop_sequences
Why it's wrong here
stop_sequences halts generation when specified strings appear; it does not alter the probability distribution over tokens, so sampling randomness is unchanged. It is genuinely useful for cutting output at delimiters or preventing runaway text. Determinism comes from lowering temperature, which flattens the distribution.
- ✓
Temperature
Why this is correct
Temperature directly scales the sampling distribution's entropy before token selection, so lowering it sharpens probabilities toward the highest-likelihood tokens and reduces output randomness. This satisfies the stem's determinism constraint, unlike top-p or token limits, which control candidate pool size or length rather than sampling randomness itself.
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max_tokens
Why it's wrong here
max_tokens caps response length, truncating output rather than reshaping the token probability distribution, so run-to-run variation persists. It is the right control for bounding cost or latency. Randomness is governed by temperature, which scales the logits before sampling.
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top_p
Why it's wrong here
top_p is nucleus sampling: lowering it narrows the candidate token pool, which does reduce randomness, so this is a genuine distractor. The stem asks for the parameter to decrease; temperature is the direct determinism control, and top_p alone leaves temperature-driven variance intact.
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