AI-102 Practice Question: Implement natural language processing solutions
Your NLP solution uses custom text classification in Azure AI Language. You need to improve the model's accuracy. Which action should you take?
⚠ Common exam trap
Many candidates confuse hyperparameter tuning (epochs, batch size) with data quality improvements, but Azure AI Language's custom text classification abstracts away most training hyperparameters, making data augmentation the only viable lever for accuracy gains.
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 more labeled training data with balanced classes.
Custom text classification in Azure AI Language relies on supervised learning, where model accuracy is directly proportional to the quantity and quality of labeled training data. Adding more labeled examples with balanced class distributions reduces bias, improves generalization, and helps the model learn distinguishing features more effectively, which is the most impactful action for improving 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.
- ✓
Add more labeled training data with balanced classes.
Why this is correct
More labeled data improves accuracy.
- ✗
Increase the number of training epochs.
Why it's wrong here
May lead to overfitting.
- ✗
Decrease the batch size.
Why it's wrong here
May not improve accuracy.
- ✗
Use a different pretrained model.
Why it's wrong here
Custom text classification uses your own model.
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