- A
The compute temperature of GPU hardware during inference, affecting speed
Why wrong: GPU temperature is hardware monitoring — in Azure OpenAI, temperature is a model parameter controlling output randomness.
- B
A parameter controlling output randomness — low values are deterministic, high values are creative
Temperature tunes creativity vs. consistency — low temperature for accurate factual responses, high for varied creative outputs.
- C
The time limit before a model inference request times out
Why wrong: Request timeouts are API configuration — temperature is a sampling parameter affecting token selection probabilities.
- D
The sensitivity of the model's content filter — higher blocks more content
Why wrong: Content filter sensitivity is a separate content safety setting — temperature affects output diversity, not safety filtering.
What Is the Temperature Parameter in Azure OpenAI?
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 'temperature' parameter in Azure OpenAI and how does it affect output?
Quick Answer
The correct answer is that the temperature parameter in Azure OpenAI controls output randomness, where low values produce deterministic results and high values enable creative variation. This works by scaling the probability distribution over possible next tokens before sampling: a low temperature (approaching 0.0) makes the model consistently choose the most likely token, while a high temperature (1.0 or above) flattens the distribution, allowing less probable tokens to be selected and yielding more diverse, imaginative responses. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to fine-tune generative AI outputs for different use cases—for example, a customer service chatbot needs low temperature for predictable answers, while a creative writing tool benefits from high temperature. A common trap is confusing temperature with top-p (nucleus sampling); remember that temperature adjusts the entire probability curve, while top-p cuts off the least likely tokens. Memory tip: think of temperature like a thermostat for creativity—low keeps things steady, high lets the ideas boil over.
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 parameter controlling output randomness — low values are deterministic, high values are creative
Option B is correct because the temperature parameter in Azure OpenAI controls the randomness of the model's output. A low temperature (e.g., 0.0) makes the model deterministic, always choosing the most likely next token, while a high temperature (e.g., 1.0 or above) increases randomness, allowing for more creative and varied responses. This parameter directly influences the probability distribution over tokens before 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 compute temperature of GPU hardware during inference, affecting speed
Why it's wrong here
GPU temperature is hardware monitoring — in Azure OpenAI, temperature is a model parameter controlling output randomness.
- ✓
A parameter controlling output randomness — low values are deterministic, high values are creative
Why this is correct
Temperature tunes creativity vs. consistency — low temperature for accurate factual responses, high for varied creative outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The time limit before a model inference request times out
Why it's wrong here
Request timeouts are API configuration — temperature is a sampling parameter affecting token selection probabilities.
- ✗
The sensitivity of the model's content filter — higher blocks more content
Why it's wrong here
Content filter sensitivity is a separate content safety setting — temperature affects output diversity, not safety filtering.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the term 'temperature' with physical hardware temperature or time-based limits, since the word has common meanings outside of AI, leading them to pick options A or C.
Trap categories for this question
Command / output trap
GPU temperature is hardware monitoring — in Azure OpenAI, temperature is a model parameter controlling output randomness.
Detailed technical explanation
How to think about this question
Under the hood, temperature scales the logits (raw scores) output by the model before applying softmax to produce a probability distribution. A temperature of 1.0 leaves logits unchanged; values less than 1.0 sharpen the distribution (making high-probability tokens even more likely), while values greater than 1.0 flatten it (giving lower-probability tokens a better chance). In real-world scenarios, low temperature is used for factual tasks like code generation or data extraction, while high temperature is used for creative writing or brainstorming.
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: A parameter controlling output randomness — low values are deterministic, high values are creative — Option B is correct because the temperature parameter in Azure OpenAI controls the randomness of the model's output. A low temperature (e.g., 0.0) makes the model deterministic, always choosing the most likely next token, while a high temperature (e.g., 1.0 or above) increases randomness, allowing for more creative and varied responses. This parameter directly influences the probability distribution over tokens before 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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Last reviewed: Jun 11, 2026
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