Quick Answer
The answer is temperature. This parameter directly controls the randomness of generated text by scaling the logits (raw scores) before the softmax function converts them into a probability distribution over the token vocabulary. A lower temperature, such as 0.1, sharpens the distribution, making the model highly deterministic by favoring the most likely tokens, while a higher temperature, like 1.5, flattens it, allowing less probable tokens to be selected and producing more diverse or creative outputs. On the AWS Certified AI Practitioner AIF-C01 exam, this concept tests your understanding of inference parameters that shape model behavior, often appearing in scenario-based questions where you must choose the right setting to reduce repetition or increase novelty. A common trap is confusing temperature with top-p or top-k sampling, which also influence randomness but through different mechanisms. Remember: temperature is the heat dial—low for focused, high for wild.
This AIF-C01 practice question tests your understanding of applications of foundation models. 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.
Which parameter controls the randomness of generated text in a foundation model?
top_p
Why wrong: top_p controls nucleus sampling, also affects randomness but temperature is the primary parameter.
stop sequences
Why wrong: Stop sequences cause early termination, not randomness.
max_tokens
Why wrong: max_tokens limits output length, not randomness.
temperature
Temperature directly affects 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
Temperature is the correct parameter because it directly controls the randomness of token sampling in a foundation model. A lower temperature (e.g., 0.1) makes the model more deterministic by concentrating probability mass on the most likely tokens, while a higher temperature (e.g., 1.5) flattens the probability distribution, increasing the likelihood of less probable tokens and thus generating more diverse or creative outputs.
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 controls nucleus sampling, also affects randomness but temperature is the primary parameter.
stop sequences
Why it's wrong here
Stop sequences cause early termination, not randomness.
max_tokens
Why it's wrong here
max_tokens limits output length, not randomness.
temperature
Why this is correct
Temperature directly affects randomness.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the distinction between temperature (which reshapes the probability distribution) and top_p (which truncates the token set), leading candidates to confuse 'randomness control' with 'diversity via cumulative probability threshold'.
Trap categories for this question
Command / output trap
max_tokens limits output length, not randomness.
Detailed technical explanation
Under the hood, temperature scales the logits (raw scores) before applying the softmax function: logits = logits / temperature. A temperature of 0 is not used in practice because it would cause division by zero; instead, models typically implement greedy decoding (always picking the highest probability token) when temperature approaches 0. In real-world scenarios, a common pitfall is using temperature=0.0 in an API call, which may silently default to greedy decoding or raise an error depending on the provider.
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
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FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
The correct answer is: temperature — Temperature is the correct parameter because it directly controls the randomness of token sampling in a foundation model. A lower temperature (e.g., 0.1) makes the model more deterministic by concentrating probability mass on the most likely tokens, while a higher temperature (e.g., 1.5) flattens the probability distribution, increasing the likelihood of less probable tokens and thus generating more diverse or creative outputs.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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