AI-103 Computer Vision Practice Question
You are training a Custom Vision multiclass classification model for retail products. After training, you notice that the precision is high, but the recall is low for a specific product category. What does low recall indicate?
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 is missing many actual instances of that product category (high false negatives).
Low recall indicates that the model has a high false negative rate—it misses actual instances of the product category.
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 training dataset had zero images for that category.
Why it's wrong here
If zero images were present, training would fail.
- ✗
The model is incorrectly predicting that product when it is not present (high false positives).
Why it's wrong here
High false positives correspond to low precision, not low recall.
- ✓
The model is missing many actual instances of that product category (high false negatives).
Why this is correct
Recall measures the model's ability to find all actual positive instances; low recall means many true items were missed.
- ✗
The probability threshold is set too high for all classes uniformly.
Why it's wrong here
Threshold tuning affects precision/recall balance, but low recall specifically points to missed true positives.
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.