The answer is logprobs, which stands for log probabilities, and it is the correct Azure OpenAI feature for measuring token-level confidence. When the model generates code snippets, logprobs outputs the logarithm of the probability assigned to each token, giving developers a numerical measure of how certain the model was when selecting that specific word or character. On the AI-900 exam, this concept tests your understanding of how Azure OpenAI provides transparency into its generative process, often appearing in questions about evaluating model reliability or debugging unexpected outputs. A common trap is confusing logprobs with overall response confidence or with temperature settings—remember that logprobs are per-token, not per-response. For a memory tip, think of “log” as a “logbook” that records the model’s certainty for each token, helping you trace exactly where the model was confident or hesitant in its code generation.
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 software company uses Azure OpenAI to generate code snippets. They want to evaluate how confident the model is in each token it generates. Which Azure OpenAI feature provides a numerical measure of confidence for each generated token?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Logprobs
Logprobs (log probabilities) is the Azure OpenAI feature that provides a numerical measure of confidence for each generated token. It outputs the log probability of each token being selected by the model, allowing developers to assess how certain the model is about its predictions at the token level.
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.
✓
Logprobs
Why this is correct
Logprobs provides log probabilities for each token, allowing calculation of confidence levels.
Related concept
Read the scenario before looking for a memorised answer.
✗
Temperature
Why it's wrong here
Temperature adjusts the randomness of predictions but does not provide confidence scores for individual tokens.
✗
Top-p
Why it's wrong here
Top-p (nucleus sampling) controls the set of tokens considered but does not output confidence values.
✗
Presence penalty
Why it's wrong here
Presence penalty penalizes tokens that have already appeared to reduce repetition, not measure confidence.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse hyperparameters that control generation behavior (temperature, top-p, presence penalty) with output features that provide model confidence metrics, leading them to pick a parameter that influences randomness rather than the one that reports token-level probabilities.
Trap categories for this question
Command / output trap
Top-p (nucleus sampling) controls the set of tokens considered but does not output confidence values.
Detailed technical explanation
How to think about this question
Logprobs are computed during the softmax layer of the transformer model, where each token's raw logit is converted into a probability distribution. The log probability (logprobs) is the natural logarithm of that probability, and negative values closer to zero indicate higher confidence. In practice, developers can use logprobs to detect low-confidence generations, such as when the model is uncertain about a code snippet's syntax or logic, enabling fallback or retry mechanisms.
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: Logprobs — Logprobs (log probabilities) is the Azure OpenAI feature that provides a numerical measure of confidence for each generated token. It outputs the log probability of each token being selected by the model, allowing developers to assess how certain the model is about its predictions at the token level.
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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