- A
The maximum percentage of the context window used for generating output
Why wrong: Context window allocation is managed separately — top_p is a token sampling parameter that limits which tokens are considered.
- B
Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature
Top_p=0.9 means only consider tokens that together hold 90% probability mass — adapting the selection pool to the distribution.
- C
The probability threshold above which the model considers a response correct
Why wrong: Response correctness is evaluation — top_p controls sampling diversity, not whether a response is considered correct.
- D
A parameter setting the minimum confidence before the model outputs a response
Why wrong: Confidence thresholds are used in classification — top_p is a generative sampling parameter for language model token selection.
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 'top_p' (nucleus sampling) in Azure OpenAI and how does it differ from temperature?
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
Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature
Option B is correct because top_p (nucleus sampling) in Azure OpenAI controls diversity by selecting tokens from the smallest set whose cumulative probability exceeds the threshold p, rather than sampling from the full probability distribution. This differs from temperature, which scales the logits before the softmax to flatten or sharpen the distribution; top_p dynamically cuts off the long tail of low-probability tokens, providing an alternative method to control randomness without affecting the relative ranking of high-probability tokens.
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.
- ✗
The maximum percentage of the context window used for generating output
Why it's wrong here
Context window allocation is managed separately — top_p is a token sampling parameter that limits which tokens are considered.
- ✓
Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature
Why this is correct
Top_p=0.9 means only consider tokens that together hold 90% probability mass — adapting the selection pool to the distribution.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The probability threshold above which the model considers a response correct
Why it's wrong here
Response correctness is evaluation — top_p controls sampling diversity, not whether a response is considered correct.
- ✗
A parameter setting the minimum confidence before the model outputs a response
Why it's wrong here
Confidence thresholds are used in classification — top_p is a generative sampling parameter for language model token selection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse top_p with a confidence or correctness threshold, when in fact it is a sampling parameter that controls the diversity of token selection by truncating the probability distribution.
Detailed technical explanation
How to think about this question
Under the hood, top_p works by sorting the token probabilities in descending order and summing them until the cumulative probability reaches p (e.g., 0.9), then sampling only from that subset. This avoids the 'dead zone' issue where temperature scaling can still assign non-zero probability to very unlikely tokens; in practice, setting top_p=0.9 often yields more coherent outputs than low temperature alone because it dynamically prunes the distribution. A real-world scenario: when generating code completions, a low top_p (e.g., 0.1) forces the model to pick only the most likely tokens, reducing syntax errors but risking repetitive outputs, while a higher top_p (e.g., 0.95) allows more creative but potentially less reliable completions.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature — Option B is correct because top_p (nucleus sampling) in Azure OpenAI controls diversity by selecting tokens from the smallest set whose cumulative probability exceeds the threshold p, rather than sampling from the full probability distribution. This differs from temperature, which scales the logits before the softmax to flatten or sharpen the distribution; top_p dynamically cuts off the long tail of low-probability tokens, providing an alternative method to control randomness without affecting the relative ranking of high-probability tokens.
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 11, 2026
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