AI-102 Practice Question: Implement natural language processing solutions
You are developing a solution that uses Azure AI Language to analyze customer feedback. You need to determine whether the sentiment of a given sentence is positive, negative, or neutral. Which Azure AI Language feature should you use?
⚠ Common exam trap
Candidates often confuse Key Phrase Extraction with Sentiment Analysis because both seem to 'analyze' text, but Key Phrase Extraction only identifies topics or terms, not the emotional polarity of the content.
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. This feature uses machine learning classifiers trained on large datasets to assign a sentiment label and confidence scores at the sentence and document level, directly matching the requirement to analyze customer feedback for sentiment polarity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Sentiment Analysis
Why this is correct
Sentiment Analysis in Azure AI Language returns per-sentence labels of positive, negative or neutral together with confidence scores. It directly satisfies the requirement to classify each sentence's sentiment, unlike key phrase extraction or entity recognition, which return different output types.
- ✗
Entity Recognition
Why it's wrong here
Entity Recognition identifies people, places, and organisations within text, producing no sentiment score. It is tempting because feedback often mentions products and brands, but extracting those named entities is a separate task from polarity classification.
- ✗
Language Detection
Why it's wrong here
Language Detection returns the detected language code of input text, not its sentiment. It is tempting when feedback arrives in multiple languages, but identifying the language is a prerequisite step, not the polarity classification the scenario requires.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns salient terms, not sentiment polarity, so it cannot classify a sentence as positive, negative, or neutral. It is tempting for summarising feedback themes, where extracting main topics from reviews is the actual goal.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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