AIF-C01 Fundamentals of Generative AI Practice Question
A media company is experimenting with an Amazon Bedrock text model to draft short news summaries. The team notices that when they ask the same question twice, the model returns noticeably different wording each time, and sometimes the summary drifts off topic. They want more deterministic, focused responses without retraining the model. Which combination of inference parameters should they adjust to reduce randomness and keep the output on topic?
⚠ Common exam trap
The trap here is mixing up output length and delivery controls, such as maxTokens or streaming, with sampling controls that actually govern randomness and topical focus.
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
✓
Decrease the temperature and set a lower topP value.
Randomness and topical drift in foundation model output are governed by sampling parameters. Temperature scales the probability distribution, and topP restricts sampling to the smallest set of tokens whose cumulative probability exceeds the threshold. Lowering both concentrates generation on the most likely tokens, yielding more consistent and focused summaries without any model retraining.
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 temperature and increase the topP value.
Why it's wrong here
Raising temperature and topP increases randomness and broadens the pool of candidate tokens considered at each step, which would make responses more varied and more likely to drift. The team wants the opposite effect, so this combination directly conflicts with the goal of deterministic, focused summaries.
- ✓
Decrease the temperature and set a lower topP value.
Why this is correct
Lowering temperature sharpens the probability distribution so high-probability tokens are favored, and reducing topP narrows nucleus sampling to fewer likely tokens. Together they make output more deterministic and on topic, which matches the team's goal without any retraining or change to the model itself.
- ✗
Enable streaming responses and lower the topK value to zero.
Why it's wrong here
Streaming only changes how tokens are delivered to the client, not how they are selected. Setting topK to zero is not a valid meaningful configuration for reducing randomness and can produce undefined or error behavior. This option confuses response delivery mechanics with sampling controls that actually shape output determinism.
- ✗
Increase maxTokens and decrease the stop sequence length.
Why it's wrong here
maxTokens controls the maximum length of the generated response, and stop sequences define where generation halts. Neither parameter influences the randomness or topical focus of the wording. Adjusting them changes how long the summary is, not how varied or on-topic the generated text becomes.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.