AI-103 Text Analysis Practice Question
You are training a custom text classification model in Language Studio. After reviewing your trained model's performance metrics, you decide to improve recall for a specific underperforming category. What action should you take?
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 varied training examples for that category to the dataset and retrain the model.
To improve recall (capturing more of the true positive instances), you should add more diverse training examples containing that category and ensure existing labels are consistently applied.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the confidence threshold parameter in your client application code.
Why it's wrong here
Client-side threshold changes filter outputs but do not improve model training recall.
- ✗
Delete all training documents for that category.
Why it's wrong here
Deleting training data will remove the model's ability to learn that category entirely.
- ✗
Switch the classification type from multi-label to single-label.
Why it's wrong here
Changing classification architecture without addressing data volume will not inherently improve recall.
- ✓
Add more varied training examples for that category to the dataset and retrain the model.
Why this is correct
Adding more representative training samples helps the model recognize more true positive instances, improving recall.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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