Quick Answer
The correct adjustment is to increase the frequency penalty. This parameter directly penalizes tokens that have already appeared in the generated text, reducing the model’s tendency to repeat the same phrases or words. By raising the frequency penalty, you encourage more diverse output, which naturally shortens overly long descriptions and breaks redundant loops—exactly what the e-commerce company needs. On the AWS Certified AI Practitioner AIF-C01 exam, this concept tests your understanding of how inference parameters control output quality, often appearing in scenario-based questions where you must distinguish between frequency penalty (which targets repetition) and presence penalty (which encourages new topics). A common trap is confusing these two: remember, frequency penalty scales with how often a token appears, while presence penalty applies a flat penalty once a token is used. Memory tip: think “frequency = frequent flier penalty”—the more a word flies by, the harder it gets penalized.
This AIF-C01 practice question tests your understanding of applications of foundation models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
An e-commerce company uses Amazon Bedrock to generate product descriptions. They notice the descriptions are too long and contain repetitive phrases. Which parameter adjustment can help?
Increase frequency penalty
Frequency penalty reduces the likelihood of repeating tokens.
Increase temperature
Why wrong: Temperature increases randomness, not repetition control.
Increase top_p
Why wrong: Top_p controls nucleus sampling, not repetition directly.
Decrease presence penalty
Why wrong: Reducing presence penalty would increase repetition.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase frequency penalty
Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or tokens, directly addressing the issue of repetitive language in generated product descriptions. This parameter penalizes tokens that have already appeared in the text, encouraging more diverse output and naturally shortening overly long descriptions by avoiding redundant loops.
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.
Increase frequency penalty
Why this is correct
Frequency penalty reduces the likelihood of repeating tokens.
Related concept
Read the scenario before looking for a memorised answer.
Increase temperature
Why it's wrong here
Temperature increases randomness, not repetition control.
Increase top_p
Why it's wrong here
Top_p controls nucleus sampling, not repetition directly.
Decrease presence penalty
Why it's wrong here
Reducing presence penalty would increase repetition.
Common exam traps
AWS often tests the distinction between frequency penalty and presence penalty, where candidates confuse 'penalizing repetition' with 'reducing randomness' and incorrectly choose temperature or top_p adjustments.
Detailed technical explanation
The frequency penalty is applied as a linear decay to the logits of tokens that have already been generated, typically scaled by a hyperparameter (e.g., 0.0 to 1.0). For each occurrence of a token, its logit is reduced by the penalty value times the number of times it has appeared, making it progressively less likely to be chosen again. In contrast, the presence penalty applies a fixed penalty regardless of frequency, so it discourages repetition but does not differentiate between a word used twice versus ten times, making frequency penalty more effective for eliminating repetitive phrases.
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: Increase frequency penalty — Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or tokens, directly addressing the issue of repetitive language in generated product descriptions. This parameter penalizes tokens that have already appeared in the text, encouraging more diverse output and naturally shortening overly long descriptions by avoiding redundant loops.
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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