The answer is to increase the frequency penalty parameter. This parameter works by applying a penalty proportional to how often a token or phrase has already appeared in the generated text, directly addressing the need to reduce repetition frequency penalty in Azure OpenAI outputs. For the AI-900 exam, this question tests your understanding of how to control model creativity and diversity through specific parameters, often appearing in scenarios about content generation or summarization. A common trap is confusing frequency penalty with presence penalty—remember that frequency penalty targets repeated tokens specifically, while presence penalty penalizes any token that has appeared at all, regardless of frequency. On the exam, think of it this way: if the model keeps saying the same thing, you need to “fine” it for each repeat, which is exactly what increasing the frequency penalty does. A useful memory tip is “frequency fights familiarity”—the more a word shows up, the harder it is for it to appear again.
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. 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 writer uses Azure OpenAI Service to generate multiple story ideas. They find that the model often repeats the same concepts across different outputs. Which parameter should they increase to reduce repetition and encourage more novel content?
Why wrong: Temperature controls the randomness of predictions; higher values make output more random but do not specifically penalize repeated content.
B
Top_p (nucleus sampling)
Why wrong: Top_p controls the cumulative probability of token choices, affecting diversity similarly to temperature, but does not directly address repetition.
C
Frequency penalty
Frequency penalty decreases the likelihood of tokens that have already been used, directly reducing repetition and encouraging novelty.
D
Max tokens
Why wrong: Max tokens sets the maximum length of the generated response, which does not affect repetition within the text.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Frequency penalty
The frequency penalty parameter in Azure OpenAI Service reduces the likelihood of repeating the same tokens or phrases by applying a penalty proportional to the frequency of tokens already generated. Increasing this value discourages the model from reusing common concepts, thereby promoting more novel and diverse story ideas.
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.
✗
Temperature
Why it's wrong here
Temperature controls the randomness of predictions; higher values make output more random but do not specifically penalize repeated content.
✗
Top_p (nucleus sampling)
Why it's wrong here
Top_p controls the cumulative probability of token choices, affecting diversity similarly to temperature, but does not directly address repetition.
✓
Frequency penalty
Why this is correct
Frequency penalty decreases the likelihood of tokens that have already been used, directly reducing repetition and encouraging novelty.
Related concept
Read the scenario before looking for a memorised answer.
✗
Max tokens
Why it's wrong here
Max tokens sets the maximum length of the generated response, which does not affect repetition within the text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse frequency penalty with temperature or top_p, assuming that increasing randomness (temperature) or narrowing sampling (top_p) is the primary way to reduce repetition, when in fact frequency penalty is the parameter explicitly designed for that purpose.
Trap categories for this question
Similar concept trap
Top_p controls the cumulative probability of token choices, affecting diversity similarly to temperature, but does not directly address repetition.
Command / output trap
Temperature controls the randomness of predictions; higher values make output more random but do not specifically penalize repeated content.
Detailed technical explanation
How to think about this question
Under the hood, the frequency penalty subtracts a value proportional to the number of times a token has already appeared in the generated sequence from its logit score before sampling. This is applied per token, making repeated tokens less likely to be selected. In contrast, the presence penalty applies a fixed penalty regardless of frequency, which can also reduce repetition but is less targeted. A real-world scenario is generating a list of unique product names, where a high frequency penalty ensures each name is distinct.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Frequency penalty — The frequency penalty parameter in Azure OpenAI Service reduces the likelihood of repeating the same tokens or phrases by applying a penalty proportional to the frequency of tokens already generated. Increasing this value discourages the model from reusing common concepts, thereby promoting more novel and diverse story ideas.
What should I do if I get this AI-900 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.