AI-102 Practice Question: Implement natural language processing solutions
A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model accuracy?
⚠ Common exam trap
The trap here is that candidates often jump to a threshold adjustment (Option D) as a quick fix, but Azure's custom text classification models require data quality improvements first, as confidence thresholds only affect prediction output, not model accuracy.
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
✓
Review the training data for the 'billing' category and correct any mislabeled examples.
False positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.
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 training data to all categories to improve overall model performance.
Why it's wrong here
Adding more data without addressing mislabeling may not reduce false positives for 'billing'.
- ✗
Use a different Azure AI service, such as key phrase extraction, to identify billing-related content.
Why it's wrong here
Key phrase extraction is not designed for classification.
- ✓
Review the training data for the 'billing' category and correct any mislabeled examples.
Why this is correct
Correcting mislabeled examples improves the model's ability to distinguish categories.
- ✗
Increase the confidence threshold for the 'billing' category to reduce false positives.
Why it's wrong here
This is a post-processing step that does not fix the model; it may also miss true positives.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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