Quick Answer
The answer is temperature, as it directly controls the randomness of a model’s output by scaling the probability distribution over possible tokens. When temperature is set low, the model becomes more deterministic, consistently choosing the most likely next token; when set high, it flattens the distribution, allowing less likely tokens to be selected and increasing creative or varied responses. On the AWS Certified AI Practitioner AIF-C01 exam, this parameter is frequently tested in the context of Amazon Bedrock and foundation model inference, often as a distractor against top_p (nucleus sampling) or max_tokens. A common trap is confusing temperature with top_p, but remember: temperature adjusts the “spread” of probabilities, while top_p dynamically cuts off the least probable tokens. For a quick memory tip, think of a thermostat—low temperature keeps things predictable and “cool,” while high temperature makes the output “hot” and unpredictable.
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer is testing different prompts for a text generation model on Amazon Bedrock. Which parameter controls the randomness of the model's output?
top_p
Why wrong: top_p is used for nucleus sampling, which also controls diversity but is different from temperature.
stop_sequences
Why wrong: Stop sequences indicate when to stop generation, not randomness.
temperature
Temperature directly scales the logits before softmax, controlling the randomness of token selection.
max_tokens
Why wrong: max_tokens limits the number of tokens in the output, not randomness.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
temperature
Option D is correct because temperature controls the randomness of the model's predictions. Lower values make output more deterministic; higher values increase randomness. Option A (max_tokens) controls output length. Option B (top_p) is nucleus sampling. Option C (stop_sequences) defines stopping criteria.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
top_p
Why it's wrong here
top_p is used for nucleus sampling, which also controls diversity but is different from temperature.
stop_sequences
Why it's wrong here
Stop sequences indicate when to stop generation, not randomness.
temperature
Why this is correct
Temperature directly scales the logits before softmax, controlling the randomness of token selection.
Related concept
Read the scenario before looking for a memorised answer.
max_tokens
Why it's wrong here
max_tokens limits the number of tokens in the output, not randomness.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
max_tokens limits the number of tokens in the output, not randomness.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
The correct answer is: temperature — Option D is correct because temperature controls the randomness of the model's predictions. Lower values make output more deterministic; higher values increase randomness. Option A (max_tokens) controls output length. Option B (top_p) is nucleus sampling. Option C (stop_sequences) defines stopping criteria.
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
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.
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