AI-102 Practice Question: Implement knowledge mining and document intelligence solutions
A company is building a knowledge mining solution using Azure AI Search. They need to extract key phrases from a large set of documents in multiple languages. Which skill should they add to the skillset?
⚠ Common exam trap
It's easy for candidates to confuse Entity Recognition (which extracts single-word entities like 'Microsoft') with Key Phrase Extraction (which extracts multi-word phrases like 'Azure AI Search'), leading them to choose Option D instead of A.
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 skill
The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract the most important phrases from text, which directly supports the requirement to extract key phrases from documents. Azure AI Search's built-in Key Phrase Extraction skill leverages natural language processing to analyze text and return a list of key phrases, making it the appropriate skill for this knowledge mining solution.
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 skill
Why this is correct
The Key Phrase Extraction skill uses natural language processing to identify salient terms and phrases, and it supports multiple languages within Azure AI Search skillsets. It satisfies the requirement to extract key phrases from a large multilingual document set.
- ✗
Sentiment Analysis skill
Why it's wrong here
Sentiment Analysis returns positive, negative or neutral scores for opinions, not key phrases. It would be correct where the requirement is gauging tone in reviews or feedback, but extracting key phrases across multiple languages requires the Key Phrase Extraction skill, which identifies salient terms per document.
- ✗
Language Detection skill
Why it's wrong here
Language Detection returns a language code per document; it identifies language but extracts no phrases. It would be correct as a preceding step where downstream skills need the detected locale, but the stated requirement is extracting key phrases, which Language Detection cannot produce.
- ✗
Entity Recognition skill
Why it's wrong here
Entity Recognition returns people, places, organisations and other typed entities, not key phrases. It would be correct where the requirement is identifying and classifying named entities such as companies or locations, but the stem asks for salient phrase extraction, which Entity Recognition does not perform.
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