- A
Increase the frequency_penalty parameter.
Correct. Increasing frequency_penalty reduces the likelihood that the model will repeat the same tokens, making it effective against repetitive loops.
- B
Decrease the temperature parameter.
Why wrong: Decreasing temperature makes the model more deterministic and less random, but it does not specifically target repetition; it may reduce creativity and still produce loops.
- C
Increase the presence_penalty parameter.
Why wrong: Increasing presence_penalty penalizes any token that has appeared at all, which can reduce topic repetition but frequency_penalty is more precise for repetitive phrases.
- D
Decrease the top_p parameter.
Why wrong: Decreasing top_p makes the model consider only a smaller set of likely tokens, which can make outputs more focused but does not directly address repetition of already used tokens.
Quick Answer
The correct answer is to increase the frequency_penalty parameter. This parameter works by applying a penalty to tokens that have already appeared in the generated text, reducing the model’s tendency to repeat the same phrases or fall into looping patterns. Unlike the temperature or top_p parameters, which control randomness and diversity, frequency_penalty specifically targets repetition without altering the model’s overall creativity or coherence. On the AI-900 exam, this concept tests your understanding of how Azure OpenAI’s content filtering and generation controls solve real-world issues like repetitive output, often appearing in scenario-based questions about marketing or content generation. A common trap is confusing frequency_penalty with presence_penalty—remember that frequency_penalty scales with how often a token has been used, while presence_penalty penalizes any token that has appeared at all. Memory tip: think “frequency = frequency of repeats,” so increasing it pushes the model to avoid saying the same thing twice.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. 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. A key principle to apply: frequency_penalty reduces the likelihood of repeating tokens already in the output.. 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 uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains repetitive phrases or gets stuck in loops. They want to reduce this behavior without changing the overall creativity of the model. Which parameter should they adjust?
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 frequency_penalty parameter.
Increasing the frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. This directly addresses the repetitive loops without altering the overall creativity, as frequency_penalty specifically targets token frequency rather than randomness or diversity.
Key principle: frequency_penalty reduces the likelihood of repeating tokens already in the output.
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 frequency_penalty parameter.
Why this is correct
Correct. Increasing frequency_penalty reduces the likelihood that the model will repeat the same tokens, making it effective against repetitive loops.
Related concept
frequency_penalty reduces the likelihood of repeating tokens already in the output.
- ✗
Decrease the temperature parameter.
Why it's wrong here
Decreasing temperature makes the model more deterministic and less random, but it does not specifically target repetition; it may reduce creativity and still produce loops.
- ✗
Increase the presence_penalty parameter.
Why it's wrong here
Increasing presence_penalty penalizes any token that has appeared at all, which can reduce topic repetition but frequency_penalty is more precise for repetitive phrases.
- ✗
Decrease the top_p parameter.
Why it's wrong here
Decreasing top_p makes the model consider only a smaller set of likely tokens, which can make outputs more focused but does not directly address repetition of already used tokens.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse frequency_penalty with presence_penalty, assuming both reduce repetition equally, but frequency_penalty specifically targets repeated occurrences while presence_penalty only discourages topic reuse.
Trap categories for this question
Keyword trap
Increasing presence_penalty penalizes any token that has appeared at all, which can reduce topic repetition but frequency_penalty is more precise for repetitive phrases.
Command / output trap
Decreasing top_p makes the model consider only a smaller set of likely tokens, which can make outputs more focused but does not directly address repetition of already used tokens.
Detailed technical explanation
How to think about this question
Frequency_penalty works by subtracting a value proportional to the token's existing count from its logit score before sampling, effectively making repeated tokens less likely to be chosen. In contrast, presence_penalty applies a fixed penalty once a token appears, regardless of how many times it repeats. A real-world scenario is generating product descriptions where the model might overuse a brand name; increasing frequency_penalty (e.g., to 0.5) reduces that without making the text less creative.
KKey Concepts to Remember
- frequency_penalty reduces the likelihood of repeating tokens already in the output.
- It helps prevent generative AI models from getting stuck in loops.
- Increasing frequency_penalty promotes more diverse and varied text generation.
- This parameter is crucial for maintaining output quality in creative text generation tasks.
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
frequency_penalty reduces the likelihood of repeating tokens already in the output.
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. frequency_penalty reduces the likelihood of repeating tokens already in the output. 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.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — frequency_penalty reduces the likelihood of repeating tokens already in the output..
What is the correct answer to this question?
The correct answer is: Increase the frequency_penalty parameter. — Increasing the frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. This directly addresses the repetitive loops without altering the overall creativity, as frequency_penalty specifically targets token frequency rather than randomness or diversity.
What should I do if I get this AI-900 question wrong?
Review frequency_penalty reduces the likelihood of repeating tokens already in the output., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
frequency_penalty reduces the likelihood of repeating tokens already in the output.
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Last reviewed: Jun 11, 2026
This AI-900 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-900 exam.
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