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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 directly provides sentiment labels and scores.

  • Entity Recognition

    Why it's wrong here

    Entity Recognition identifies entities like people, organizations, etc.

  • Language Detection

    Why it's wrong here

    Language Detection identifies the language of the text.

  • Key Phrase Extraction

    Why it's wrong here

    Key Phrase Extraction extracts key phrases but does not analyze sentiment.

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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.