- A
The percentage of training images that contained this type of object
Why wrong: Training data statistics are dataset properties — confidence score measures the model's certainty about a specific detection.
- B
The model's certainty about a detection, used to set thresholds balancing false positives vs misses
Confidence scores enable threshold tuning — higher thresholds reduce false positives; lower thresholds reduce misses.
- C
The accuracy of the model measured on the test dataset during training
Why wrong: Test accuracy is a training evaluation metric — confidence score is a per-inference certainty value.
- D
A quality rating assigned by human reviewers to confirm the detection is correct
Why wrong: Human verification is a review process — confidence scores are automatically generated by the model during inference.
Quick Answer
The answer is a numerical value from 0 to 1 representing the model’s certainty that a detected object is correctly identified and localized. This confidence score is calculated by the Custom Vision algorithm based on how closely the detected region matches learned features from training data, allowing you to set a threshold to balance false positives (low-confidence detections) against misses (true objects falling below the cutoff). On the AI-900 exam, this concept tests your understanding of how to interpret prediction outputs and tune model behavior for real-world use—a common trap is confusing confidence score with training accuracy or dataset size. Remember, a higher threshold reduces false alarms but may miss valid objects, while a lower threshold catches more but risks false positives. Memory tip: think of it as a “certainty dial” that you adjust to match your tolerance for mistakes.
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 does 'confidence score' mean in Azure AI Custom Vision object detection results?
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
The model's certainty about a detection, used to set thresholds balancing false positives vs misses
In Azure AI Custom Vision, the confidence score is a numerical value (0 to 1) that represents the model's certainty that a detected object is correctly identified and localized. This score allows you to set a threshold to filter out low-certainty detections, balancing false positives (detections with low confidence) against misses (true objects that fall below the threshold). It is not a measure of training data composition, test accuracy, or human review.
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 that contained this type of object
Why it's wrong here
Training data statistics are dataset properties — confidence score measures the model's certainty about a specific detection.
- ✓
The model's certainty about a detection, used to set thresholds balancing false positives vs misses
Why this is correct
Confidence scores enable threshold tuning — higher thresholds reduce false positives; lower thresholds reduce misses.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The accuracy of the model measured on the test dataset during training
Why it's wrong here
Test accuracy is a training evaluation metric — confidence score is a per-inference certainty value.
- ✗
A quality rating assigned by human reviewers to confirm the detection is correct
Why it's wrong here
Human verification is a review process — confidence scores are automatically generated by the model during inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the confidence score with overall model accuracy or training data statistics, when in fact it is a per-prediction certainty value used to filter results.
Detailed technical explanation
How to think about this question
Under the hood, the confidence score in Custom Vision is derived from the softmax output of the neural network's final layer, representing the probability that the predicted bounding box contains the specified object. In object detection, the model also uses non-maximum suppression (NMS) to eliminate duplicate detections, and the confidence threshold directly influences which boxes survive NMS. In a real-world scenario, setting a high threshold (e.g., 0.9) reduces false positives but may miss true objects, while a low threshold (e.g., 0.3) catches more objects but increases false alarms—this trade-off is critical in applications like autonomous driving or security surveillance.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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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: The model's certainty about a detection, used to set thresholds balancing false positives vs misses — In Azure AI Custom Vision, the confidence score is a numerical value (0 to 1) that represents the model's certainty that a detected object is correctly identified and localized. This score allows you to set a threshold to filter out low-certainty detections, balancing false positives (detections with low confidence) against misses (true objects that fall below the threshold). It is not a measure of training data composition, test accuracy, or human review.
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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Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'model confidence score' in Azure Custom Vision predictions?
medium- A.The percentage of training images the model correctly labelled during training
- ✓ B.A per-prediction certainty measure indicating how sure the model is about a specific classification
- C.A rating of the training data quality provided by the annotation team
- D.Microsoft's certification level for how well a Custom Vision model meets enterprise standards
Why B: 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.
Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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