Question 902 of 1,020

Describe features of computer vision workloads on Azure →mediumMultiple ChoiceObjective-mapped

AI-900 Practice Question: Describe features of computer vision workloads on Azure

This AI-900 practice question tests your understanding of describe features of computer vision 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 'model confidence score' in Azure Custom Vision predictions?

Question 1mediummultiple choice

Full question →

A
The percentage of training images the model correctly labelled during training
Why wrong: Training accuracy is a model evaluation metric — confidence score is a per-prediction certainty measure at inference time.
B
A per-prediction certainty measure indicating how sure the model is about a specific classification
Confidence scores let applications set thresholds — accepting high-confidence predictions and routing low-confidence ones for human review.
C
A rating of the training data quality provided by the annotation team
Why wrong: Data quality scores are annotation metrics — confidence score is generated by the model for each individual inference request.
D
Microsoft's certification level for how well a Custom Vision model meets enterprise standards
Why wrong: Enterprise certification is a governance concept — confidence score is a technical inference output indicating prediction certainty.

Full breakdown with real-world context →

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 per-prediction certainty measure indicating how sure the model is about a specific classification

In Azure Custom Vision, the model confidence score is a per-prediction value (ranging from 0 to 1) that quantifies the model's certainty that a given input image belongs to a specific class. It is computed during inference based on the probability distribution output by the trained classifier, not during training. This score helps users decide whether to accept or reject a prediction based on a custom threshold.

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 percentage of training images the model correctly labelled during training
Why it's wrong here
Training accuracy is a model evaluation metric — confidence score is a per-prediction certainty measure at inference time.
✓
A per-prediction certainty measure indicating how sure the model is about a specific classification
Why this is correct
Confidence scores let applications set thresholds — accepting high-confidence predictions and routing low-confidence ones for human review.
Related concept
Read the scenario before looking for a memorised answer.
✗
A rating of the training data quality provided by the annotation team
Why it's wrong here
Data quality scores are annotation metrics — confidence score is generated by the model for each individual inference request.
✗
Microsoft's certification level for how well a Custom Vision model meets enterprise standards
Why it's wrong here
Enterprise certification is a governance concept — confidence score is a technical inference output indicating prediction certainty.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse training accuracy (how well the model performed on the training set) with the per-prediction confidence score, leading them to select Option A instead of recognizing that confidence is a real-time inference measure.

Trap categories for this question

Command / output trap
Enterprise certification is a governance concept — confidence score is a technical inference output indicating prediction certainty.

Detailed technical explanation

How to think about this question

Under the hood, the confidence score is derived from the softmax function applied to the final layer logits, producing a probability vector over all tags. A subtle behavior is that the score can be high even for out-of-distribution images if the model is overconfident, so practitioners often set a minimum probability threshold (e.g., 0.5 or 0.7) to filter low-confidence predictions in production. In a real-world scenario, a retail app using Custom Vision to detect defective products might only act on predictions with confidence > 0.9 to avoid false positives.

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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision 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 per-prediction certainty measure indicating how sure the model is about a specific classification — In Azure Custom Vision, the model confidence score is a per-prediction value (ranging from 0 to 1) that quantifies the model's certainty that a given input image belongs to a specific class. It is computed during inference based on the probability distribution output by the trained classifier, not during training. This score helps users decide whether to accept or reject a prediction based on a custom threshold.

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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