AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are building an Azure AI Search enrichment pipeline that must extract text and layout information from scanned PDFs stored in Azure Blob Storage. The extracted content must include bounding boxes for each text line so that a downstream custom skill can associate key-value pairs spatially. You need to add a built-in skill to the skillset to perform this extraction. Which skill should you add?
⚠ Common exam trap
The trap here is assuming that any text extraction skill returns layout coordinates, when in fact only DocumentIntelligenceLayoutSkill provides bounding boxes for scanned PDFs.
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
✓
DocumentIntelligenceLayoutSkill
The requirement is to extract text and layout information, including bounding boxes, from scanned PDFs. DocumentIntelligenceLayoutSkill is a built-in Azure AI Search skill that uses Azure AI Document Intelligence to analyze document structure and return bounding boxes for text lines. This enables downstream spatial association. Other skills either do not provide layout data or are not designed for direct PDF analysis, so they fail to meet the spatial requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DocumentExtractionSkill
Why it's wrong here
DocumentExtractionSkill extracts text and images from files such as PDFs and Office documents, but it does not return bounding box coordinates for individual text lines. It is useful for basic text extraction in an enrichment pipeline, yet the requirement here explicitly demands spatial data for key-value association. Since the skill lacks layout coordinates, it cannot satisfy the spatial association need in this scenario.
- ✗
TextMergeSkill
Why it's wrong here
TextMergeSkill concatenates text from multiple fields or documents into a single string. It does not analyze document layout or extract bounding boxes. This skill is useful for combining text before indexing, but it cannot provide the spatial information needed to associate key-value pairs. Since the scenario requires bounding boxes from scanned PDFs, TextMergeSkill is not applicable.
- ✗
OcrSkill
Why it's wrong here
OcrSkill performs optical character recognition on images and can return text and layout information, but it is designed for image files, not directly for PDFs. For a PDF, you would typically need to extract embedded images first or use a document extraction skill. While OcrSkill can provide bounding boxes, using it alone on scanned PDFs without prior extraction would not reliably process multi-page PDFs. Thus it is not the best fit here.
- ✓
DocumentIntelligenceLayoutSkill
Why this is correct
DocumentIntelligenceLayoutSkill is a built-in Azure AI Search skill that leverages Azure AI Document Intelligence to extract text, layout, and structure from documents, including scanned PDFs. It returns bounding boxes for text lines and other layout elements, enabling spatial association in downstream skills. This skill is specifically designed for document layout analysis, making it the correct choice to meet the requirement for bounding box coordinates.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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