AI-900 Practice Question: Describe features of generative AI workloads on Azure
What is 'temperature' parameter in Azure OpenAI and how does it affect output?
⚠ Common exam trap
Candidates often 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.
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
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.
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 hardware temperature is a physical metric for cooling and hardware monitoring, unrelated to model generation. Azure OpenAI abstracts the underlying infrastructure, and the temperature parameter is a hyperparameter that modifies logits for token sampling. Inference speed depends on model size, request batch, and GPU capacity, but the temperature value has no effect on throughput or latency.
- ✓
A parameter controlling output randomness — low values are deterministic, high values are creative
Why this is correct
Temperature is a sampling parameter in Azure OpenAI that controls output randomness by scaling logits before the softmax layer. Low values (e.g., 0.0) make the model deterministically select the most likely next token, while higher values (e.g., 1.0) flatten the probability distribution, allowing more creative or varied responses. It does not influence content filtering, latency, or API timeouts; it only governs token selection.
- ✗
The time limit before a model inference request times out
Why it's wrong here
A request timeout is an API client configuration that limits how long to wait for a response, independent of generation settings. Temperature does not control the duration of an inference call; it only changes the probability distribution over possible next tokens. Timeouts are set by the caller for resource management, while temperature is a per-request sampling parameter that affects output variety.
- ✗
The sensitivity of the model's content filter — higher blocks more content
Why it's wrong here
Content filter sensitivity is a separate safety configuration in Azure OpenAI, not a sampling parameter. Temperature alters the token probability distribution, whereas the content filter uses the AI Content Safety service to block disallowed categories like hate or violence. Even with a low temperature, the filter still evaluates the generated text independently, so raising temperature does not make the filter stricter.
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Azure Machine Learning Studio
Key term
Token
A token is a digital or physical object that represents a set of permissions or access rights, used to verify identity or grant access in computer systems.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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