This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are using Azure OpenAI Service to generate text. You need to reduce the likelihood of the model generating repetitive sequences. Which TWO parameters should you adjust?
frequency_penalty
Frequency penalty reduces repetition by penalizing frequent tokens.
max_tokens
Why wrong: max_tokens controls output length.
top_p
Why wrong: top_p controls nucleus sampling, not repetition.
temperature
Why wrong: Temperature controls creativity, not repetition.
presence_penalty
Presence penalty reduces repetition by penalizing tokens that have appeared.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
frequency_penalty
Frequency penalty reduces the likelihood of the model repeating the same tokens or phrases by subtracting a fixed penalty from the log-probability of tokens that have already appeared in the generated text. This directly discourages repetitive sequences, making it one of the two correct parameters to adjust.
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.
frequency_penalty
Why this is correct
Frequency penalty reduces repetition by penalizing frequent tokens.
Related concept
Read the scenario before looking for a memorised answer.
max_tokens
Why it's wrong here
max_tokens controls output length.
top_p
Why it's wrong here
top_p controls nucleus sampling, not repetition.
temperature
Why it's wrong here
Temperature controls creativity, not repetition.
presence_penalty
Why this is correct
Presence penalty reduces repetition by penalizing tokens that have appeared.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse temperature and top_p with repetition control, but these parameters affect randomness and diversity of token selection, not the direct penalization of repeated tokens.
Trap categories for this question
Command / output trap
max_tokens controls output length.
Detailed technical explanation
Under the hood, frequency_penalty works by adding a value proportional to the number of times a token has already appeared in the sequence to the logit of that token, reducing its probability of being selected again. Presence_penalty, the other correct parameter, applies a flat penalty once a token appears, regardless of frequency, making it effective for encouraging topic diversity. In real-world scenarios, combining both penalties is often used to balance between avoiding repetition and maintaining coherent, non-redundant output.
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
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: frequency_penalty — Frequency penalty reduces the likelihood of the model repeating the same tokens or phrases by subtracting a fixed penalty from the log-probability of tokens that have already appeared in the generated text. This directly discourages repetitive sequences, making it one of the two correct parameters to adjust.
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 24, 2026
This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.
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