Implement information extraction solutions
Free Microsoft Azure AI Apps & Agents Developer (AI-103) practice — 6 questions on Implement information extraction solutions, with explanations. No sign-up. Full 12-question mixed test →
A financial services company is building a RAG solution to analyze quarterly reports containing tables, charts, and handwritten annotations. The ingestion pipeline must extract tabular data with row/column relationships preserved, recognize handwritten notes, and chunk the content semantically for vector search. The solution must minimize custom code and integrate directly with Azure AI Search enrichment. Which combination of services provides the MOST complete solution?
Content Understanding (part of Azure AI Foundry) provides production-grade semantic chunking and clean grounded representations specifically designed for RAG scenarios. Document Intelligence layout model extracts tables with structure preserved and handles handwritten text. Azure AI Search custom skills orchestrate the enrichment pipeline, connecting Document Intelligence → Content Understanding → vector indexing in a single managed flow without custom chunking code.
An enterprise knowledge management system ingests 500,000 internal documents (Word, PDF, PowerPoint) into Azure AI Search. The retrieval pipeline must support hybrid search combining BM25 keyword matching with vector similarity, while enriching documents with custom taxonomy tags using a proprietary classification model hosted as an Azure ML endpoint. During query time, results must be re-ranked using semantic ranker. What is the MINIMUM set of configuration steps required?
Vector profiles (containing HNSW algorithm config) are mandatory for vector search fields. Custom skills in the indexer skillset enable calling the Azure ML endpoint during enrichment. Semantic ranker requires L2 (Standard) tier or higher and is invoked via queryType=semantic. Hybrid queries combine 'vector' and 'search' parameters in a single request—both ranking signals merge automatically.
A healthcare application extracts patient form data containing checkboxes, signatures, and typed text from scanned PDFs. The extraction must identify form fields by their labels (e.g., "Patient Name:"), preserve checkbox states (checked/unchecked), and achieve >98% accuracy on typed alphanumeric text. Extracted data populates an agent's working memory for patient intake workflows. The solution must minimize training time. Which Azure AI Document Intelligence approach meets these requirements?
Custom extraction models (formerly custom neural models) are purpose-built for form field extraction with labeled training. They directly recognize field labels, extract associated values, and identify selection marks (checkboxes) with field-level confidence. 50+ samples provide sufficient training for high accuracy on consistent form layouts. Output structured JSON maps directly to agent working memory schemas, and training time is measured in hours, not weeks.
A media company processes video interviews for an AI agent that answers questions about speaker statements. The ingestion pipeline must transcribe audio with speaker labels, extract key frames showing on-screen text or graphics, and index both modalities for hybrid retrieval. When a user asks 'What did the CEO say about Q3 revenue?', the agent must cite the specific timestamp and show the relevant frame. The pipeline must handle 4K video files up to 2 hours in length. Which architecture satisfies all requirements?
Azure Video Indexer natively handles speaker diarization (speaker labels), keyframe extraction, on-screen text OCR (video OCR), and generates time-coded insights—all in one service. Outputs JSON with timestamps linking transcripts to frames. Azure AI Search custom skills can enrich this data, creating a unified searchable index. Agents query once and get correlated text + visual citations with exact timestamps. Handles 2-hour 4K videos within service limits.
A legal tech company's RAG system ingests 10,000 contracts daily. Each contract must be analyzed for specific clauses (termination, liability, payment terms), with clause text and page numbers preserved. The enrichment pipeline currently uses Azure AI Document Intelligence layout model followed by a custom Azure Function that calls GPT-4 to classify clauses, taking 45 seconds per document. Costs must be reduced by 60% while maintaining accuracy. Which optimization provides the GREATEST cost reduction?
Azure AI Language custom text classification provides trained classification models with per-text-record pricing (typically $0.001–0.002 per record) vs GPT-4 token-based pricing ($0.03–0.06 per 1K tokens for multiple clause extractions). Integrated as an Azure AI Search skill, it processes during indexing without per-query LLM calls. Classification models handle clause categorization at 98%+ accuracy when properly trained. Cost reduction >60% is achievable: ~$0.002 per doc vs ~$0.015–0.03 GPT-4 cost. Latency drops to <1 second per document.
An e-commerce agent uses RAG to answer product questions. The ingestion pipeline indexes 2 million product descriptions and images. Users ask visual questions like 'Do you have a blue jacket similar to this photo?' The system must retrieve products using both text (description match) and visual similarity (image match), then re-rank results by combining both signals. Image vectors are 1024-dimensional CLIP embeddings. The solution must support filters on category and price range during retrieval. What is the correct Azure AI Search index configuration?
Hybrid search in Azure AI Search combines BM25 text search with vector similarity in a single query. Text fields (product description) use standard text search. Vector field stores CLIP image embeddings (1024-dim) with HNSW for efficient approximate nearest neighbor search. Filters apply before retrieval for performance. RRF (default in hybrid queries) automatically merges text and vector ranking signals into a unified score. This is the native, production-ready pattern for multi-modal RAG.
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