AI-102 Practice Question: Implement natural language processing solutions
A company uses Azure AI Language Service to analyze customer feedback. They notice that the sentiment scores for negative reviews are often incorrectly labeled as neutral. Which configuration should be adjusted to improve accuracy?
⚠ Common exam trap
Test-takers frequently assume adjusting a confidence threshold (option B) can fix misclassification, but this only changes the decision boundary for the existing model, not the model's ability to distinguish sentiment in your specific data.
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
✓
Create a custom sentiment analysis model using Custom Text Classification
The Azure AI Language Service's pre-built sentiment analysis model may not capture domain-specific nuances in customer feedback, leading to misclassification of negative reviews as neutral. By creating a custom sentiment analysis model using Custom Text Classification (option C), you can train the model on your labeled data to improve accuracy for your specific use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the Language service in a different Azure region
Why it's wrong here
Region does not affect model accuracy.
- ✗
Increase the confidence threshold for sentiment classification
Why it's wrong here
This would only change sensitivity, not domain relevance.
- ✓
Create a custom sentiment analysis model using Custom Text Classification
Why this is correct
A custom model can be trained on domain-specific data to improve accuracy.
- ✗
Enable Key Phrase Extraction to preprocess the text
Why it's wrong here
Key Phrase Extraction does not improve sentiment analysis.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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