- A
Decrease the temperature parameter to 0.1
Why wrong: Temperature controls the randomness of token selection; reducing it makes outputs more deterministic but does not directly filter out low-probability tokens. It may still choose a very improbable token if it has a high rank.
- B
Set the top_p parameter to a value like 0.9
Top_p (nucleus sampling) filters out low-probability tokens by only considering the smallest set of tokens whose cumulative probability is >= top_p. This reduces the chance of nonsensical words while allowing creativity from the remaining higher-probability tokens.
- C
Increase the stop parameter to include more stop sequences
Why wrong: Stop sequences tell the model when to end generation, they do not influence which tokens are selected during generation.
- D
Increase the max_tokens parameter to allow longer descriptions
Why wrong: Max_tokens limits the length of the output; it does not affect the probability distribution of token selection.
Quick Answer
The answer is to set the top_p parameter to a value like 0.9. This technique, known as top p or nucleus sampling, works by instructing the model to consider only the tokens whose cumulative probability mass reaches 90%, effectively cutting off very low-probability, nonsensical words while still allowing creative variability from the top 90% of likely tokens. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to balance output coherence with creativity in Azure OpenAI Service, often appearing as a scenario where a developer wants to reduce nonsense without making responses fully deterministic. A common trap is confusing top_p with temperature—remember that top_p controls the pool of candidate tokens, while temperature scales their probabilities. For a quick memory tip, think of top p as a "probability cutoff" that trims the tail of unlikely words, ensuring the model stays on track while still offering 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.
A developer uses Azure OpenAI Service to generate short product descriptions. The developer notices that the model sometimes produces nonsensical or very low-probability words that make the output less coherent. The developer wants to reduce the chance of such outputs while still allowing some creative variability. Which parameter should the developer adjust in the API request?
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
Set the top_p parameter to a value like 0.9
Option B is correct because setting `top_p` to 0.9 (nucleus sampling) instructs the model to consider only the tokens whose cumulative probability mass reaches 90%, thereby cutting off very low-probability (nonsensical) tokens while still allowing creative variability from the top 90% of likely tokens. This directly addresses the developer's goal of reducing incoherent outputs without fully deterministic generation.
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.
- ✗
Decrease the temperature parameter to 0.1
Why it's wrong here
Temperature controls the randomness of token selection; reducing it makes outputs more deterministic but does not directly filter out low-probability tokens. It may still choose a very improbable token if it has a high rank.
- ✓
Set the top_p parameter to a value like 0.9
Why this is correct
Top_p (nucleus sampling) filters out low-probability tokens by only considering the smallest set of tokens whose cumulative probability is >= top_p. This reduces the chance of nonsensical words while allowing creativity from the remaining higher-probability tokens.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the stop parameter to include more stop sequences
Why it's wrong here
Stop sequences tell the model when to end generation, they do not influence which tokens are selected during generation.
- ✗
Increase the max_tokens parameter to allow longer descriptions
Why it's wrong here
Max_tokens limits the length of the output; it does not affect the probability distribution of token selection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse temperature (which controls randomness uniformly) with top_p (which controls the cumulative probability cutoff), and incorrectly assume lowering temperature is the only way to reduce nonsensical outputs, ignoring that top_p can achieve the same goal while preserving more creative variability.
Trap categories for this question
Command / output trap
Temperature controls the randomness of token selection; reducing it makes outputs more deterministic but does not directly filter out low-probability tokens. It may still choose a very improbable token if it has a high rank.
Detailed technical explanation
How to think about this question
Under the hood, `top_p` (nucleus sampling) dynamically selects a set of tokens whose cumulative probability exceeds the threshold (e.g., 0.9), then redistributes probability mass only among those tokens before sampling. This is different from temperature scaling, which uniformly scales logits; `top_p` can better adapt to contexts where the probability distribution is flat (many plausible tokens) versus peaked (few plausible tokens), making it more robust for maintaining coherence while allowing creativity.
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: Set the top_p parameter to a value like 0.9 — Option B is correct because setting `top_p` to 0.9 (nucleus sampling) instructs the model to consider only the tokens whose cumulative probability mass reaches 90%, thereby cutting off very low-probability (nonsensical) tokens while still allowing creative variability from the top 90% of likely tokens. This directly addresses the developer's goal of reducing incoherent outputs without fully deterministic generation.
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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Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'top_p' (nucleus sampling) in Azure OpenAI and how does it differ from temperature?
medium- A.The maximum percentage of the context window used for generating output
- ✓ B.Restricting token selection to those whose cumulative probability reaches p — an alternative diversity control to temperature
- C.The probability threshold above which the model considers a response correct
- D.A parameter setting the minimum confidence before the model outputs a response
Why B: 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.
Last reviewed: Jun 11, 2026
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