- A
Reduce the probability threshold to increase recall.
Why wrong: This trades off precision for recall, not robustness.
- B
Capture additional images under the new lighting and retrain the model.
Adding representative data from the new conditions is the best practice.
- C
Use Azure AutoML to automatically find the best algorithm.
Why wrong: AutoML is for tabular data, not Custom Vision.
- D
Add more images from the original lighting conditions to the training set.
Why wrong: This does not address the new lighting conditions.
Question 470 of 993
Handle Custom Vision Domain Shift Caused by Lighting Changes
This AI-102 practice question tests your understanding of implement computer vision solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Under the hood, Custom Vision uses a transfer-learned deep neural network (e.g., ResNet or EfficientNet) that learns hierarchical features from images. When lighting conditions change, the feature distribution shifts—for example, edge contrasts and color histograms differ—causing the model's learned decision boundaries to misclassify. Retraining with augmented or new data from the target domain (new lighting) allows the model to adjust its internal weights via backpropagation, effectively performing domain adaptation. In real-world production lines, this is often combined with data augmentation (e.g., brightness, contrast adjustments) to further improve robustness without requiring as many new images.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Capture additional images under the new lighting and retrain the model. — Option B is correct because 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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