AI-102 Plan and manage an Azure AI solution Practice Question
You deploy a custom vision model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse 'too few images' (a contributing factor) with the direct diagnosis of 'overfitting,' but the question asks for the most likely cause of the described symptom, which is the overfitting itself, not its root cause.
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 overfitted to the training data
High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.
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 time was too short
Why it's wrong here
Short training time usually causes underfitting.
- ✗
The training dataset has too few images
Why it's wrong here
Too few images typically causes underfitting, not overfitting.
- ✗
The wrong domain was selected during training
Why it's wrong here
Wrong domain may affect features but not specifically cause overfitting.
- ✓
The model is overfitted to the training data
Why this is correct
Overfitting leads to high training accuracy but poor generalization.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.