AI-102 Implement agentic AI solutions Practice Question
An agent uses Azure AI Language to perform sentiment analysis on customer feedback. The team notices that the sentiment scores are sometimes inaccurate for negative feedback. Which TWO improvements should the team consider?
⚠ Common exam trap
It's easy for candidates to assume that increasing precision or switching models generically will fix inaccuracies, rather than recognizing that domain-specific fine-tuning and text pre-processing are the standard Azure AI Language approaches to handle linguistic edge cases like negations and sarcasm.
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
✓
Pre-process the text to handle negations and sarcasm.
Azure AI Language's pre-built sentiment analysis models can struggle with linguistic nuances like negations (e.g., 'not good') and sarcasm (e.g., 'Great, another delay'). Pre-processing the text to explicitly handle these patterns—such as by expanding contractions or using negation detection—can improve the accuracy of the sentiment scores before they are passed to the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pre-process the text to handle negations and sarcasm.
Why this is correct
Pre-processing can improve sentiment detection.
- ✓
Use a custom sentiment analysis model trained on domain-specific data.
Why this is correct
Custom models can capture domain-specific nuances.
- ✗
Switch to a different language model without fine-tuning.
Why it's wrong here
Different models may not solve domain issues.
- ✗
Increase the number of decimal places in the sentiment score.
Why it's wrong here
Precision does not improve accuracy.
- ✗
Increase the confidence threshold for positive sentiment.
Why it's wrong here
Threshold does not improve model accuracy.
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