AI-102 Plan and manage an Azure AI solution Practice Question
A company is using Azure AI Vision to analyze images from a manufacturing line. The solution must detect defects in real-time. The team discovers that the model's accuracy drops significantly when images are captured under different lighting conditions. What is the best approach to improve the model's robustness?
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
✓
Retrain the model using images captured under various lighting conditions, using data augmentation.
Retraining the model with images captured under various lighting conditions, along with data augmentation, directly exposes the model to diverse lighting scenarios, improving its robustness. Option A is incorrect because while image pre-processing can help normalize lighting, it may not fully compensate for the lack of varied training data, and the model may still not generalize well to unseen lighting conditions. Option B is incorrect because simply increasing the number of training images without varying lighting conditions does not help the model learn to handle different lighting; it only reinforces the existing bias. Option D is incorrect because using a pre-built model from Azure AI Vision may not be tailored for the specific defect detection task and may still suffer from sensitivity to lighting changes without retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply image pre-processing to normalize lighting before sending to the model.
Why it's wrong here
Pre-processing helps but does not address the root cause if the model was not trained on varied data.
- ✗
Increase the number of training images without varying lighting conditions.
Why it's wrong here
More images without variation will not help the model generalize to different lighting.
- ✓
Retrain the model using images captured under various lighting conditions, using data augmentation.
Why this is correct
Including diverse lighting in training data and using augmentation improves robustness.
- ✗
Use a pre-built model from Azure AI Vision instead of a custom model.
Why it's wrong here
Pre-built models are not specialized for defect detection in manufacturing.
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Written by Johnson Ajibi, MSc IT Security
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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.