AI-102 Plan and manage an Azure AI solution Practice Question
You are deploying an Azure AI Language service custom text classification model. You need to ensure that the training data is balanced and representative. What should you do?
⚠ Common exam trap
Many candidates confuse data balancing techniques (like oversampling) with the fundamental requirement of a representative train-test split, leading them to choose Option B or D instead of recognizing that stratified splitting is the direct and correct method for ensuring balanced and representative data in Azure AI Language custom text classification.
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
✓
Split the labeled data into training and test sets, ensuring each class has similar proportions.
Splitting labeled data into training and test sets while ensuring each class has similar proportions (stratified split) is a standard practice for balanced and representative training data. This approach prevents class imbalance from skewing model evaluation metrics and ensures the model generalizes well to unseen data. In Azure AI Language custom text classification, this is critical for achieving reliable performance across all classes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use only the most frequent labels to train the model.
Why it's wrong here
Ignoring minority classes results in a biased model.
- ✗
Oversample the minority classes to match the majority class size.
Why it's wrong here
Oversampling can lead to overfitting and does not guarantee representativeness.
- ✓
Split the labeled data into training and test sets, ensuring each class has similar proportions.
Why this is correct
A ensures balanced representation and proper evaluation.
- ✗
Use all labeled data for training and rely on cross-validation.
Why it's wrong here
Cross-validation without a held-out test set can overestimate performance.
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