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AIF-C01 Practice Question: A data scientist is using Amazon Bedrock to…
A data scientist is using Amazon Bedrock to generate product descriptions. They discover that the model frequently repeats phrases and produces overly deterministic outputs. Which parameter adjustment would MOST likely introduce more diversity?
⚠ Common exam trap
AWS often tests the misconception that increasing top_p or decreasing top_k increases diversity, when in fact both adjustments can reduce diversity by narrowing the token selection pool, whereas temperature is the primary hyperparameter for controlling randomness.
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 temperature from 0.7 to 1.2
Increasing the temperature from 0.7 to 1.2 raises the randomness of token selection by flattening the probability distribution, which makes lower-probability tokens more likely to be chosen. This directly counters the overly deterministic and repetitive outputs by introducing more diversity into the generated text.
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 top_p value from 0.9 to 1.0
Why it's wrong here
Raising top_p from 0.9 to 1.0 widens the nucleus to include nearly all probability mass, which usually increases randomness rather than fixing repetition; the deterministic behaviour stems from low temperature. It is tempting because top_p controls the sampling pool, but temperature is the parameter governing output determinism.
- ✓
Increase the temperature from 0.7 to 1.2
Why this is correct
Raising temperature to 1.2 flattens the softmax probability distribution over the vocabulary, so lower-probability tokens are sampled more often, directly countering the repetition and determinism described. The stem's constraint is insufficient output diversity; temperature is the parameter controlling sampling randomness, making this the most direct adjustment.
- ✗
Decrease the top_k value from 50 to 10
Why it's wrong here
Lower top_k restricts token choices, often making outputs more repetitive.
- ✗
Decrease the temperature from 0.7 to 0.2
Why it's wrong here
Reducing temperature from 0.7 to 0.2 sharpens the probability distribution, making the model pick high-probability tokens and amplifying the deterministic, repetitive behaviour. It is tempting because temperature is the obvious diversity control, but lowering it moves in the wrong direction; increasing temperature would introduce diversity.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.