Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are a machine learning engineer at a retail company. The company wants to build a product knowledge base by extracting information from product manuals, specifications sheets, and customer reviews. The data sources include PDFs, Word documents, and plain text files stored in Azure Blob Storage. The solution must: (1) extract product name, model number, price, and key features; (2) analyze customer reviews to extract sentiment and common issues; (3) enable natural language queries like 'Which products have the best reviews under $100?'; (4) handle documents in English and Spanish. You need to design a solution using Azure AI Search and Azure AI Services. Which approach meets all requirements with the least development effort?
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
✓
Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.
It uses a single Azure AI Search pipeline with a skillset that includes Document Layout skill (to handle various document formats like PDFs, Word docs, and text files), Text Translation skill (to convert Spanish documents to English, unifying the language), Sentiment skill (to analyze customer reviews for sentiment), and Key Phrase Extraction skill (to extract key features and common issues). Enabling semantic search allows natural language queries such as 'Which products have the best reviews under $100?'. This approach meets all requirements with the least development effort, as it avoids the need for multiple pipelines or custom model training required by other options.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure AI Document Intelligence custom model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.
Why it's wrong here
Requires two pipelines; no translation for Spanish.
- ✓
Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.
Why this is correct
Single pipeline handles all document types, translates, extracts sentiment, and enables natural language queries.
- ✗
Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.
Why it's wrong here
OCR is not needed for digital documents; Entity Recognition may not extract key features well.
- ✗
Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.
Why it's wrong here
Document Intelligence may not handle plain text files well; no sentiment analysis.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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