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.
Option A is correct because Azure AI Language's prebuilt sentiment analysis can misclassify negations (e.g., "not good") and sarcasm, so pre-processing the text to normalize or flag these constructs improves accuracy. Option B is correct because training a custom sentiment analysis model on domain-specific labeled data lets the service learn industry-specific vocabulary and phrasing, which typically boosts accuracy for specialized feedback. Option C is not appropriate because simply swapping language models without fine-tuning does not address domain-specific or negation/sarcasm issues. Option D is wrong because the number of decimal places in the score is a formatting detail and does not affect model accuracy. Option E is wrong because raising a positive-sentiment confidence threshold only changes classification cutoffs and does not improve the underlying sentiment detection for negative feedback.
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
Negation and sarcasm invert surface polarity, so the model scores positive words as positive despite negative intent. Pre-processing rewrites or flags these constructions before scoring, directly addressing the inaccurate negative-feedback scores described in the stem.
- ✓
Use a custom sentiment analysis model trained on domain-specific data.
Why this is correct
Training a custom sentiment analysis model on domain-specific feedback lets Azure AI Language learn your terminology, slang and negation patterns, directly addressing the inaccurate negative scores. The built-in model generalises poorly to niche vocabulary, whereas custom training satisfies the stem's requirement for improved accuracy on your own customer feedback.
- ✗
Switch to a different language model without fine-tuning.
Why it's wrong here
Swapping models without fine-tuning leaves the same generic sentiment weights that misread domain-specific negative phrasing, so accuracy on your feedback stays unchanged. It appeals because Azure AI Language offers several prebuilt and custom model types, and a different base model would be the right move only if the current one lacked support for your language or text format.
- ✗
Increase the number of decimal places in the sentiment score.
Why it's wrong here
Sentiment scores are model outputs whose precision is fixed by the service; adding decimal places merely reformats the same value and cannot correct misclassification. It is tempting because the scores look imprecise, but the correct improvement is supplying domain-specific training data or adjusting the model.
- ✗
Increase the confidence threshold for positive sentiment.
Why it's wrong here
Raising the positive threshold reclassifies borderline positive text as neutral or negative; it never corrects a genuinely negative sentence scored positive, so the underlying misclassification persists. Threshold tuning is the right lever when you need to trade precision against recall for a chosen class, not when the model's polarity judgement itself is wrong.
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