- A
Increase the top-p value to 0.9
Higher top-p allows sampling from a larger cumulative probability mass, increasing diversity.
- B
Increase the top-k value to 50
Higher top-k expands the number of candidate tokens considered, promoting diversity.
- C
Decrease the top-p value to 0.1
Why wrong: Lower top-p restricts the candidate pool, reducing diversity.
- D
Set the seed to a fixed value
Why wrong: Fixed seed makes outputs deterministic, reducing diversity.
- E
Increase the temperature
Higher temperature increases randomness, producing more diverse outputs.
Question 113 of 1,000
AIF-C01 Temperature Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: temperature. 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 data science team is using Amazon Bedrock to generate synthetic data for training a new model. They need to ensure the generated data is diverse and covers edge cases. Which THREE parameters should they adjust to maximize diversity? (Select THREE.)
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 top-p value to 0.9
Increasing the top-p value to 0.9 allows the model to sample from a larger cumulative probability mass, which includes more diverse and less likely tokens. This increases the variety in the generated synthetic data, helping to cover edge cases. A higher top-p value is a standard technique to promote output diversity in autoregressive models.
Key principle: Temperature
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 to 0.9
Why this is correct
Higher top-p allows sampling from a larger cumulative probability mass, increasing diversity.
Related concept
Temperature
- ✓
Increase the top-k value to 50
Why this is correct
Higher top-k expands the number of candidate tokens considered, promoting diversity.
Related concept
Temperature
- ✗
Decrease the top-p value to 0.1
Why it's wrong here
Lower top-p restricts the candidate pool, reducing diversity.
- ✗
Set the seed to a fixed value
Why it's wrong here
Fixed seed makes outputs deterministic, reducing diversity.
- ✓
Increase the temperature
Why this is correct
Higher temperature increases randomness, producing more diverse outputs.
Related concept
Temperature
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that decreasing top-p or fixing the seed increases diversity, when in fact both actions reduce randomness and limit the model's ability to generate varied outputs.
Trap categories for this question
Command / output trap
Fixed seed makes outputs deterministic, reducing diversity.
Detailed technical explanation
How to think about this question
Top-p (nucleus sampling) works by sorting token probabilities in descending order and selecting the smallest set whose cumulative probability exceeds the threshold p. At p=0.9, the model samples from a larger pool of tokens, including low-probability ones, which is crucial for generating rare or edge-case data points. In contrast, temperature scales the logits before softmax; higher temperature (e.g., >1.0) flattens the probability distribution, making low-probability tokens more likely and further enhancing diversity. These parameters are often tuned together: high temperature with high top-p yields maximum stochasticity.
KKey Concepts to Remember
- Temperature
- Top-p sampling
- Top-k sampling
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Temperature
Real-world example
How this comes up in practice
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. Temperature 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
Got this wrong? Here's your next step.
Review temperature, then practise related AIF-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Temperature.
What is the correct answer to this question?
The correct answer is: Increase the top-p value to 0.9 — Increasing the top-p value to 0.9 allows the model to sample from a larger cumulative probability mass, which includes more diverse and less likely tokens. This increases the variety in the generated synthetic data, helping to cover edge cases. A higher top-p value is a standard technique to promote output diversity in autoregressive models.
What should I do if I get this AIF-C01 question wrong?
Review temperature, then practise related AIF-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Temperature
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Last reviewed: Jul 4, 2026
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