Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are building a knowledge mining solution for legal documents using Azure AI Search. The solution must extract entities like dates, organizations, and persons from PDF files and index them. Which built-in skill should you add to the skillset to perform this extraction?
⚠ Common exam trap
A common mix-up: candidates confuse entity extraction with key phrase extraction or OCR, mistakenly thinking that extracting 'important terms' or 'text from images' is equivalent to identifying specific named entities like dates and organizations.
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
✓
Named Entity Recognition skill
The Named Entity Recognition (NER) skill in Azure AI Search is specifically designed to extract entities such as dates, organizations, and persons from text. When added to a skillset, it processes the content extracted from PDF files and outputs structured entity information that can be indexed and queried. This directly matches the requirement to extract and index named entities from legal documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Named Entity Recognition skill
Why this is correct
NER extracts entities like persons, organizations, dates.
- ✗
Language Detection skill
Why it's wrong here
Identifies language, not entities.
- ✗
Optical Character Recognition (OCR) skill
Why it's wrong here
OCR extracts text from images, not entities.
- ✗
Key Phrase Extraction skill
Why it's wrong here
Extracts key phrases, not named entities.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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