The correct preprocessing step is to resize all images to the same fixed dimensions, such as 224x224 pixels, before training. This is required because Azure Custom Vision models, particularly those based on ResNet architectures, expect a consistent input tensor shape; varying image resolutions would cause shape mismatches in the neural network, leading to training failures or severely degraded accuracy. On the AI-102 exam, this concept tests your understanding of how convolutional neural networks demand uniform input sizes, often appearing in scenario-based questions where you must choose between resizing, cropping, or padding. A common trap is selecting “pad to largest dimension” instead of resizing, but padding introduces unnecessary blank space that can distort feature learning. Remember the memory tip: “Fixed input, fixed output—resize to 224 before you deploy.”
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?
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.
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.
✓
Resize all images to the same dimensions (e.g., 224x224).
Why this is correct
Custom Vision expects consistent image sizes for optimal performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, Azure Custom Vision uses convolutional neural networks (CNNs) like ResNet or MobileNet, which have fully connected layers that require a fixed input size. Resizing ensures that the spatial dimensions of feature maps align with the network's architecture. In real-world scenarios, failing to resize can cause batch processing errors or silent performance degradation because the model's input layer rejects variable-size tensors.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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