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
The Named Entity Recognition skill calls Azure AI Language to extract persons, organisations and dates from document text, writing them into the index as enriched fields. This directly satisfies the requirement to index those entity types from PDFs.
- ✗
Language Detection skill
Why it's wrong here
Language Detection only outputs a language code and confidence score for each record; it identifies no dates, organisations or persons. It belongs earlier in a pipeline, letting you route or filter content by language before language-specific enrichment skills run.
- ✗
Optical Character Recognition (OCR) skill
Why it's wrong here
OCR extracts raw text from images embedded in PDFs; it produces no entity types such as dates, organisations or persons. It is the right skill when scanned documents yield no machine-readable text and must be made searchable before enrichment.
- ✗
Key Phrase Extraction skill
Why it's wrong here
Key Phrase Extraction returns salient noun phrases, not typed entities such as dates, organisations and persons, so the index would lack the required entity fields. It suits search relevance tuning or topic discovery, where surfacing dominant terms from unstructured text is the goal.
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