AI-102 Practice Question: Implement natural language processing solutions
You are using Azure AI Language to analyze customer reviews. You need to determine whether each review expresses a positive, negative, or neutral sentiment. Which API should you call?
⚠ Common exam trap
A common mix-up: candidates confuse 'sentiment' with 'key phrases' or 'entities,' assuming that extracting important words or names can imply sentiment, but only the Sentiment Analysis API directly evaluates emotional tone.
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 API.
The Sentiment Analysis API is specifically designed to evaluate text and return sentiment labels (positive, negative, neutral) along with confidence scores. This directly matches the requirement to determine whether each customer review expresses positive, negative, or neutral 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 API.
Why it's wrong here
Language detection returns the detected language name and ISO code for the input text; it produces no sentiment score or polarity label. It is tempting because it is the correct AI Language API when the requirement is identifying whether text is English, French, or another language, but the stem asks for positive, negative, or neutral classification, which sentiment analysis provides.
- ✗
Entity recognition API.
Why it's wrong here
Entity recognition returns named entities such as people, places, and organisations with their categories; it does not output positive, negative, or neutral labels. It is tempting because entity extraction is the correct AI Language API when the requirement is identifying and classifying named things in text, but the stem asks for opinion polarity, which sentiment analysis provides.
- ✓
Sentiment analysis API.
Why this is correct
The sentiment analysis API returns per-document and per-sentence scores across positive, negative and neutral labels, directly satisfying the requirement to classify each review's polarity. Other Azure AI Language features, such as key phrase extraction or entity recognition, do not produce sentiment classifications.
- ✗
Key phrase extraction API.
Why it's wrong here
Key phrase extraction returns the salient terms in a document, not a polarity classification, so it cannot label each review positive, negative, or neutral. It is tempting because it is the correct AI Language API when the requirement is summarising what a review discusses, but the stem asks for the expressed opinion, which sentiment analysis returns.
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Same concept, more angles
1 more way this is tested on AI-102
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Variation 1. 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?
easy- ✓ A.Sentiment Analysis
- B.Entity Recognition
- C.Language Detection
- D.Key Phrase Extraction
Why A: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.