AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'model confidence score' in Azure Custom Vision predictions?
⚠ Common exam trap
A common mix-up: 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.
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.
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 global, retrospective metric computed by comparing the model's labels against ground-truth labels across the entire training dataset. In contrast, a confidence score is generated at inference time for each individual prediction, indicating that particular prediction's certainty — even a model with mediocre training accuracy can emit a high confidence score for a specific image if that image closely resembles a learned pattern.
- ✓
A per-prediction certainty measure indicating how sure the model is about a specific classification
Why this is correct
In Custom Vision, every classification or object-detection prediction returned by the predict API includes a numeric confidence score (typically 0–1) indicating the model's certainty that the input belongs to a particular tag or that a detected object is present. Applications can use these per-prediction scores to set acceptance thresholds — for example, automatically acting only on predictions above 0.90 while routing lower-confidence results to human review, thereby reducing false positives in production workflows.
- ✗
A rating of the training data quality provided by the annotation team
Why it's wrong here
Data quality ratings, such as annotation agreement or label correctness, are evaluated during dataset preparation and are independent of the trained model's outputs. A confidence score is not provided by the annotation team; it is computed dynamically by the model's final layer for each new inference request, reflecting the model's internal probability assessment rather than any external judgment about the input data's quality.
- ✗
Microsoft's certification level for how well a Custom Vision model meets enterprise standards
Why it's wrong here
There is no such certification level for Custom Vision models, and enterprise-readiness governance (e.g., compliance, SLAs) is a deployment concern that has nothing to do with a single inference's output. The confidence score is a purely technical, per-request value produced by the model's softmax (or equivalent) layer, quantifying prediction certainty rather than any organizational or certification status.
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Azure Machine Learning Studio
Key term
Prediction
Prediction is the process of using data and algorithms to forecast future outcomes or identify patterns without explicit programming for each scenario.
Key term
Confidence score
A confidence score is a number (often between 0 and 1 or 0 and 100%) that tells you how likely it is that an AI model's prediction or answer is correct.
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