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Implement computer vision solutionshardMultiple ChoiceObjective-mapped

AI-102 Implement computer vision solutions Practice Question

A manufacturing company uses Azure AI Custom Vision to detect defects on a production line. The model was trained with 500 images per class and achieves 95% accuracy. After deployment, the model's accuracy drops to 80% due to changes in lighting conditions. What is the most effective first step to improve the model's robustness?

⚠ Common exam trap

Many exam-takers confuse a performance tuning action (like adjusting the probability threshold) with a data quality fix, or assume AutoML can magically fix any accuracy drop, when in fact the root cause is a classic domain shift that requires representative retraining data.

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

Capture additional images under the new lighting and retrain the model.

The drop in accuracy is caused by a domain shift—specifically, new lighting conditions that were not represented in the original training set. The most effective first step is to capture additional images under the new lighting and retrain the model, as Custom Vision relies on diverse, representative training data to generalize to real-world variations. This directly addresses the root cause by expanding the training distribution to include the new lighting scenario, which is a fundamental principle of supervised learning in computer vision.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Reduce the probability threshold to increase recall.

    Why it's wrong here

    This trades off precision for recall, not robustness.

  • Capture additional images under the new lighting and retrain the model.

    Why this is correct

    Adding representative data from the new conditions is the best practice.

  • Use Azure AutoML to automatically find the best algorithm.

    Why it's wrong here

    AutoML is for tabular data, not Custom Vision.

  • Add more images from the original lighting conditions to the training set.

    Why it's wrong here

    This does not address the new lighting conditions.

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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.