AI-103 Computer Vision Practice Question
You are using Custom Vision to train an object detection model. You want to evaluate model performance before publishing an iteration. Which metric represents the overall accuracy of the model across all classes at various confidence thresholds?
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
✓
Mean Average Precision (mAP)
Mean Average Precision (mAP) is the standard metric used in Custom Vision and object detection to evaluate overall model accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perplexity
Why it's wrong here
Perplexity evaluates language models.
- ✗
Word Error Rate (WER)
Why it's wrong here
WER evaluates speech-to-text accuracy.
- ✗
BLEU Score
Why it's wrong here
BLEU score evaluates machine translation quality, not object detection.
- ✓
Mean Average Precision (mAP)
Why this is correct
mAP summarizes object detection accuracy across multiple classes and thresholds.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 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-103 exam.