AI-900 Practice Question: Describe features of generative AI workloads on Azure
A marketing team uses Azure OpenAI to generate social media posts. They want to ensure the generated text maintains a consistent, predictable brand voice without being overly creative or random. Which parameter should they primarily adjust to control the randomness of the output?
⚠ Common exam trap
Candidates often confuse Top P (nucleus sampling) with temperature, thinking both control randomness equally, but temperature directly scales the logits for a more fine-grained control over determinism, whereas Top P dynamically selects a subset of tokens based on cumulative probability.
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
✓
Temperature
Temperature controls the randomness of token selection by scaling the logits before applying the softmax function. A lower temperature (e.g., 0.2) makes the model more deterministic and conservative, producing outputs that stick closely to the most likely tokens—ideal for maintaining a consistent, predictable brand voice. Higher temperatures increase randomness, which the team wants to avoid.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Temperature
Why this is correct
Temperature directly controls the randomness of the model's sampling distribution by scaling the logits before the softmax layer. Lower values (e.g., 0.2) concentrate probability on the highest-likelihood tokens, producing deterministic, brand-consistent text, while higher values (e.g., 0.8) flatten the distribution for more creative and varied outputs. This makes it the standard parameter for tuning creativity versus reliability, precisely what's needed for maintaining a consistent social media voice.
- ✗
Max tokens
Why it's wrong here
Max tokens caps the total length of the generated response by limiting how many tokens the model can output, but it has no effect on the sampling distribution or the logits at each generation step. It can truncate a response mid-sentence, but it neither encourages nor restrains randomness, creativity, or consistency. Therefore, it is not the correct parameter for controlling how varied or predictable the brand's social copy will be.
- ✗
Frequency penalty
Why it's wrong here
Frequency penalty modifies the logits by reducing the likelihood of tokens that have already appeared in the generated text, with the reduction growing with each repetition. This targets repetitive phrasing by making repeated tokens less likely to be selected again, but it does not globally control the temperature or the overall entropy of the output distribution. It affects lexical diversity, not the fundamental randomness or creativity sweet spot, so it isn't the parameter for that purpose.
- ✗
Top P
Why it's wrong here
Top P (nucleus sampling) selects tokens only from the smallest set whose cumulative probability mass exceeds a threshold (e.g., 0.9), dynamically trimming the tail of the distribution. While it does influence output diversity and can reduce rare-token surprises, it acts as a filter on the distribution rather than a direct scaler of randomness. The Azure OpenAI docs designate temperature as the primary knob for randomness vs. determinism, making it the correct choice over top P.
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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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