AI-102 Practice Question: Implement natural language processing solutions
You need to analyze customer call transcripts to identify positive and negative sentiment. Which Azure AI Language feature should you use?
⚠ Common exam trap
Many exam-takers confuse Key Phrase Extraction with Sentiment Analysis, assuming that identifying key topics inherently reveals sentiment, but Key Phrase Extraction provides no sentiment polarity or confidence scores.
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
✓
Sentiment Analysis
Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to evaluate text and determine whether the sentiment expressed is positive, negative, or neutral. For customer call transcripts, this feature analyzes each sentence or document and returns a sentiment label and confidence scores, directly addressing the requirement to identify positive and negative sentiment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Language Detection
Why it's wrong here
Language Detection only identifies which language text is written in; it returns a language code, not opinion polarity. Sentiment analysis is the Azure AI Language feature that scores positive, negative and neutral sentiment. Detection is tempting when transcripts arrive in mixed languages, but it cannot classify the sentiment the scenario requires.
- ✗
Named Entity Recognition
Why it's wrong here
Named Entity Recognition extracts people, places, organisations and similar entities from text, not sentiment polarity. It is tempting because it analyses transcripts, but it identifies entity categories rather than positive or negative tone, so it cannot classify the customer sentiment the scenario requires.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns the main talking points in text, not sentiment polarity. It is tempting because it summarises transcripts, but it identifies salient phrases rather than positive or negative tone, so it cannot classify the customer sentiment the scenario requires.
- ✓
Sentiment Analysis
Why this is correct
Sentiment Analysis directly returns per-document and per-sentence sentiment labels with confidence scores, satisfying the requirement to identify positive and negative opinions in call transcripts. It is purpose-built for opinion mining within Azure AI Language, unlike key phrase extraction or entity recognition, which surface terms rather than polarity.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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