AI-102 Practice Question: Implement knowledge mining and information extraction solutions
A company uses Azure AI Search to index customer support tickets. They need to automatically extract key phrases from each ticket to improve search relevance. Which built-in skill should they add to the skillset?
⚠ Common exam trap
AI-102 often tests the difference between similar cognitive skills; the trap is confusing Key Phrase Extraction with Entity Recognition or Sentiment Analysis when the requirement is specifically to extract key phrases.
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
The Key Phrase Extraction skill in Azure AI Search is a built-in cognitive skill that uses natural language processing to extract key phrases from text. It is designed exactly for scenarios like extracting important terms from support tickets to improve search relevance and indexing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Key Phrase Extraction
Why this is correct
Key Phrase Extraction is a built-in cognitive skill that runs natural language processing over each ticket's text, returning salient terms directly into the index. It satisfies the requirement to extract key phrases automatically without custom code or model training, improving relevance through enriched searchable fields.
- ✗
Entity Recognition
Why it's wrong here
Entity recognition classifies text into categories such as people, places and organisations, not salient phrases. It is tempting because it also enriches indexed content, and it would be correct when you need to extract structured entities like company names or locations from tickets.
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment analysis scores text as positive, negative or neutral, producing a label rather than key phrases. It is tempting because it also enriches indexed content, and it would be correct when you need to gauge customer mood or route dissatisfied tickets for escalation.
- ✗
OCR
Why it's wrong here
OCR extracts printed or handwritten text from images, so it returns raw characters rather than key phrases. It is tempting because it enriches documents during indexing, and it would be correct when tickets arrive as scanned images or PDFs whose text must first be made searchable.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.