AI-102 Plan and manage an Azure AI solution Practice Question
You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?
⚠ Common exam trap
Many candidates confuse model performance tuning (threshold, iterations) with data quality issues, mistakenly believing that adjusting hyperparameters can compensate for missing training scenarios.
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
✓
Add images with different lighting conditions to the training set
The core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add images with different lighting conditions to the training set
Why this is correct
Including low-light images trains the model to handle such conditions.
- ✗
Increase the probability threshold
Why it's wrong here
Threshold adjustments affect confidence, not model robustness to lighting.
- ✗
Increase the number of training iterations
Why it's wrong here
More iterations do not compensate for lack of diverse training data.
- ✗
Use a domain-specific model for vehicles
Why it's wrong here
Domain-specific models are pre-trained but may still need diverse lighting data.
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