AI-102 Plan and manage an Azure AI solution Practice Question
A company is deploying a custom vision model using Azure Custom Vision. The training data contains images with varying resolutions. The model must achieve high accuracy. Which pre-processing step should be applied to the images before training?
⚠ Common exam trap
Microsoft often tests the misconception that normalization or augmentation alone can compensate for varying image sizes, but the core requirement is that all images must be resized to the same dimensions to satisfy the fixed input layer of the neural network.
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
✓
Resize all images to the same dimensions (e.g., 224x224).
Custom Vision models use a fixed input size (e.g., 224x224 for ResNet-based architectures). Images with varying resolutions must be resized to the same dimensions before training to ensure consistent tensor shapes for the neural network. Without this step, the model cannot process the data correctly, leading to training failures or degraded accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Resize all images to the same dimensions (e.g., 224x224).
Why this is correct
Custom Vision expects consistent image sizes for optimal performance.
- ✗
Convert images to grayscale.
Why it's wrong here
Color information may be important for the model.
- ✗
Normalize pixel values to a range of 0-1.
Why it's wrong here
Custom Vision handles normalization internally.
- ✗
Apply data augmentation techniques like random cropping.
Why it's wrong here
Data augmentation is typically done during training, not as a mandatory pre-processing step.
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