The answer is to decrease the temperature and decrease the top_p value. Decreasing the temperature lowers the probability of sampling less likely tokens, making the model more deterministic and reducing randomness, while decreasing top_p narrows the cumulative probability threshold for token selection, which still allows for some diversity within a more constrained set of choices. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how these two parameters control the trade-off between creativity and determinism in Amazon Bedrock, often appearing as a scenario where you must balance output control. A common trap is assuming you should increase temperature for less randomness, but remember that lower temperature means less randomness, while top_p acts as a diversity filter. Memory tip: think of temperature as the "focus" dial—lower it for sharper, less random results—and top_p as the "pool" size—smaller pools still have variety but fewer surprises.
AIF-C01 Fundamentals of Generative AI Practice Question
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 company is using Amazon Bedrock to generate creative marketing copy. They want to reduce the randomness of the output while maintaining diversity. Which TWO parameters should they adjust?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease the top_p value
Decreasing the temperature (Option E) reduces randomness by lowering the probability of sampling lower-ranked tokens, making the model more deterministic. Decreasing top_p (Option D) narrows the cumulative probability threshold for token selection, which also reduces randomness while still allowing some diversity within the narrowed set. Together, these parameters control the trade-off between creativity and determinism in Amazon Bedrock's text generation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Why it's wrong here
Higher temperature increases randomness.
✗
Increase the max token count
Why it's wrong here
Max tokens affects output length, not randomness.
✗
Increase the top_k value
Why it's wrong here
Increasing top_k allows more tokens, potentially increasing randomness.
✓
Decrease the top_p value
Why this is correct
Lower top_p reduces the set of possible tokens, making output less random.
Related concept
Read the scenario before looking for a memorised answer.
✓
Decrease the temperature
Why this is correct
Lower temperature makes output more deterministic.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing top_k or top_p reduces randomness, when in fact increasing either expands the token pool and can increase randomness, while decreasing them is what reduces randomness.
Trap categories for this question
Command / output trap
Max tokens affects output length, not randomness.
Detailed technical explanation
How to think about this question
Temperature scales the logits before softmax, with lower values (e.g., 0.1) making the probability distribution peak sharply on high-probability tokens, while top_p (nucleus sampling) dynamically selects the smallest set of tokens whose cumulative probability exceeds the threshold (e.g., 0.9). In Amazon Bedrock, these parameters are applied per inference call, and combining a low temperature with a moderate top_p can yield consistent yet varied outputs for marketing copy generation.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Decrease the top_p value — Decreasing the temperature (Option E) reduces randomness by lowering the probability of sampling lower-ranked tokens, making the model more deterministic. Decreasing top_p (Option D) narrows the cumulative probability threshold for token selection, which also reduces randomness while still allowing some diversity within the narrowed set. Together, these parameters control the trade-off between creativity and determinism in Amazon Bedrock's text generation.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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