The answer is a sampling method that restricts token selection to the most probable token set. This is correct because top_p, also known as nucleus sampling, works by setting a cumulative probability threshold—for example, a value of 0.9 means the model only considers the smallest group of tokens whose combined probability reaches 90%, cutting off the long tail of unlikely choices. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to control output creativity versus determinism in Azure OpenAI API calls, often appearing as a comparison to temperature or top_k sampling. A common trap is confusing top_p with temperature, but remember: temperature scales all probabilities, while top_p dynamically prunes the token pool. For a quick memory tip, think of “p” for “pool”—top_p selects a pool of tokens that together hit your probability threshold, making it a smarter way to balance coherence and variety.
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 purpose of 'top_p' (nucleus sampling) in Azure OpenAI API calls?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A sampling method that restricts token selection to the most probable token set
Option B is correct because 'top_p' (nucleus sampling) in Azure OpenAI API calls controls the cumulative probability threshold for token selection. Instead of considering all possible next tokens, the model selects from the smallest set of tokens whose cumulative probability exceeds the 'top_p' value (e.g., 0.9 means the model considers only the top tokens that together have a 90% chance). This reduces randomness while allowing more natural variation than fixed 'top_k' sampling.
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 number of paragraphs in the generated response
Why it's wrong here
Paragraph count is formatting — top_p controls the probability distribution used for token sampling.
✓
A sampling method that restricts token selection to the most probable token set
Why this is correct
Top_p (nucleus sampling) samples from the smallest token set exceeding probability threshold p — controlling output diversity without temperature.
Related concept
Read the scenario before looking for a memorised answer.
✗
A parameter that sets the minimum response quality threshold
Why it's wrong here
Quality thresholds are not a direct parameter — top_p controls statistical sampling diversity.
✗
The priority level of the API request in a queue
Why it's wrong here
Request prioritization is API management — top_p is a generation parameter for controlling output diversity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'top_p' with a simple 'top-k' count or a quality threshold, when in fact it is a cumulative probability cutoff that dynamically adjusts the candidate set size based on the model's confidence distribution.
Trap categories for this question
Command / output trap
Request prioritization is API management — top_p is a generation parameter for controlling output diversity.
Detailed technical explanation
How to think about this question
Under the hood, nucleus sampling works by sorting all candidate tokens by probability descending, then selecting the smallest set whose cumulative probability exceeds the 'top_p' threshold. For example, with top_p=0.9, if the top 5 tokens have probabilities [0.4, 0.3, 0.15, 0.05, 0.03] (cumulative 0.93), only those 5 tokens are considered, and the next token is randomly chosen from them (re-normalized). This avoids the deterministic nature of greedy decoding and the flat randomness of temperature-only sampling, making it ideal for creative tasks like story generation where you want plausible but varied outputs.
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: A sampling method that restricts token selection to the most probable token set — Option B is correct because 'top_p' (nucleus sampling) in Azure OpenAI API calls controls the cumulative probability threshold for token selection. Instead of considering all possible next tokens, the model selects from the smallest set of tokens whose cumulative probability exceeds the 'top_p' value (e.g., 0.9 means the model considers only the top tokens that together have a 90% chance). This reduces randomness while allowing more natural variation than fixed 'top_k' sampling.
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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Question Discussion
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