- A
A parameter requiring AI systems to acknowledge their presence as AI to users
Why wrong: AI disclosure is a transparency concern — presence penalty controls output vocabulary diversity.
- B
A flat penalty discouraging repetition of any token already present in the response
Presence penalty adds a flat penalty for any previously used token — encouraging vocabulary diversity regardless of repeat frequency.
- C
A parameter indicating whether the AI is present online or offline
Why wrong: Service availability is operational status — presence penalty is a generation diversity parameter.
- D
The minimum number of characters that must be present in a response
Why wrong: Response length minimums are formatting concerns — presence penalty controls output vocabulary repetition.
Quick Answer
The correct answer is that the presence penalty parameter in Azure OpenAI applies a flat penalty to any token already present in the response, discouraging repetition of that token. This works by reducing the model’s likelihood of selecting a token that has already appeared in the output sequence, thereby promoting more diverse and less repetitive text generation. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to control output variety versus coherence, often appearing alongside the frequency penalty parameter in scenario-based questions. A common trap is confusing presence penalty with frequency penalty—remember that presence penalizes any token that has appeared at all, regardless of how many times, while frequency penalizes tokens proportionally to their usage count. For a quick memory tip, think of presence as a “once is enough” flat fee, whereas frequency is a “repeat offender” progressive tax.
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.
What is the 'presence penalty' parameter in Azure OpenAI API calls?
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
A flat penalty discouraging repetition of any token already present in the response
The 'presence penalty' parameter in Azure OpenAI API calls applies a flat penalty to any token that has already appeared in the response so far, reducing the model's likelihood of repeating that token. This helps generate more diverse and less repetitive text by discouraging the reuse of tokens already present in the output sequence.
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.
- ✗
A parameter requiring AI systems to acknowledge their presence as AI to users
Why it's wrong here
AI disclosure is a transparency concern — presence penalty controls output vocabulary diversity.
- ✓
A flat penalty discouraging repetition of any token already present in the response
Why this is correct
Presence penalty adds a flat penalty for any previously used token — encouraging vocabulary diversity regardless of repeat frequency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A parameter indicating whether the AI is present online or offline
Why it's wrong here
Service availability is operational status — presence penalty is a generation diversity parameter.
- ✗
The minimum number of characters that must be present in a response
Why it's wrong here
Response length minimums are formatting concerns — presence penalty controls output vocabulary repetition.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between 'presence penalty' and 'frequency penalty' — the trap here is that candidates confuse the presence penalty with a requirement for AI disclosure or a simple repetition penalty, missing that it specifically penalizes any token that has already appeared at least once, regardless of how many times.
Trap categories for this question
Command / output trap
AI disclosure is a transparency concern — presence penalty controls output vocabulary diversity.
Detailed technical explanation
How to think about this question
Under the hood, the presence penalty subtracts a fixed value (scaled by the penalty parameter, typically between -2.0 and 2.0) from the logit of any token that has already been generated in the current response. This is applied before the softmax function, making repeated tokens less likely to be selected. In contrast, the frequency penalty scales the penalty by the number of times a token has appeared, which can be more aggressive for highly repeated tokens. A real-world scenario where presence penalty is critical is in creative writing or code generation, where avoiding repetitive phrases or variable names improves output quality.
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
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A flat penalty discouraging repetition of any token already present in the response — The 'presence penalty' parameter in Azure OpenAI API calls applies a flat penalty to any token that has already appeared in the response so far, reducing the model's likelihood of repeating that token. This helps generate more diverse and less repetitive text by discouraging the reuse of tokens already present in the output sequence.
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.
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Last reviewed: Jun 30, 2026
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