AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are building a knowledge mining solution using Azure AI Search with AI enrichment. Which TWO built-in skills can be used to extract information from images embedded in documents?
⚠ Common exam trap
Candidates often confuse the Image Analysis skill with the OCR skill, thinking only one is needed for image extraction, but the question asks for TWO skills that extract information from images—one for visual content and one for text.
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
✓
Image Analysis skill
The Image Analysis skill (B) is correct because it invokes the Computer Vision service to extract visual features from embedded images, such as descriptions, tags, and celebrity or landmark detection, which is exactly the kind of image-derived information a knowledge mining pipeline needs. The OCR skill (C) is also correct because it extracts printed and handwritten text from image files (including images embedded in documents), producing text that can be mapped into the search index. The Entity Recognition skill (A) operates on text to identify entities like people, places, and organizations, not on image content. The Key Phrase Extraction skill (D) analyzes text to surface salient phrases and does not process images. The Text Translation skill (E) translates text between languages and likewise does not extract information from images.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Entity Recognition skill
Why it's wrong here
Entity Recognition extracts entities from text, not from images, so it cannot read embedded image content. It is tempting because it is a genuine built-in cognitive skill used during AI enrichment, and would be correct for extracting people, places or organisations from document text rather than images.
- ✓
Image Analysis skill
Why this is correct
The Image Analysis skill extracts visual features and generates descriptions or tags from image content, and can also produce text via its OCR capability. It satisfies the requirement to extract information from images embedded within documents during AI enrichment.
- ✓
OCR skill
Why this is correct
The OCR skill reads text embedded in images, returning recognised characters as searchable content. It satisfies the requirement to extract information from images embedded in documents, complementing visual-feature extraction with the literal text those images contain.
- ✗
Key Phrase Extraction skill
Why it's wrong here
Key Phrase Extraction operates on text tokens to surface salient terms; it has no OCR or image-analysis capability, so embedded images yield nothing. It is tempting because it is a genuine AI enrichment skill, and it would be the right choice when condensing extracted document text into key phrases.
- ✗
Text Translation skill
Why it's wrong here
Text Translation converts text between languages; it never inspects image pixels, so it cannot extract embedded image content. It is tempting because enrichment pipelines do chain translation after OCR, and it would be correct where the requirement is localising already-extracted text into another language.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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