AI-102 Practice Question: Implement natural language processing solutions
Your application needs to extract key phrases from customer reviews to identify common topics. Which Azure AI Language feature should you use?
⚠ Common exam trap
Many candidates confuse Named Entity Recognition (NER) with Key Phrase Extraction because both deal with extracting information from text, but NER focuses on predefined entity types (e.g., persons, locations) while Key Phrase Extraction identifies any significant topic or phrase relevant to the document's 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
✓
Key Phrase Extraction
Key Phrase Extraction is the correct Azure AI Language feature because it is specifically designed to identify and return a list of key phrases from unstructured text, such as customer reviews, that capture the main topics and themes. This allows you to aggregate common topics across multiple reviews without manual analysis.
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 it's wrong here
Sentiment Analysis returns positive, negative or neutral scores per document or sentence, not topic terms. It is tempting because it also mines customer reviews for insight, and would be correct if the application needed to gauge satisfaction or flag dissatisfied customers rather than identify common subjects.
- ✗
Language Detection
Why it's wrong here
Language Detection returns the language code of input text, producing no topic output. It is tempting because it also processes unstructured review text before other analysis, and would be correct if reviews arrived in multiple languages and the application needed to route each to the matching language model.
- ✗
Named Entity Recognition
Why it's wrong here
Named Entity Recognition classifies spans into categories such as person, location and organisation, not topics. It is tempting because it also parses unstructured review text, and would be correct if the application needed to pull company or product names from those reviews rather than recurring themes.
- ✓
Key Phrase Extraction
Why this is correct
Key Phrase Extraction identifies the main talking points in unstructured text, returning salient terms and phrases. Applied to customer reviews, it surfaces recurring topics directly, matching the stated requirement without needing custom model training or entity categorisation.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 761-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.