Implement information extraction solutions

You maintain an Azure AI Search enrichment pipeline that ingests 200,000 scanned PDFs. The skillset runs OCR, a custom Web API skill for entity extraction, and text splitting. During development you frequently modify only the text-splitting skill's parameters and re-run the indexer, but every run re-executes the expensive OCR and custom Web API skills against all documents, driving up costs and runtime. You need to reduce redundant billed skill executions across iterative runs while keeping unchanged upstream enrichments intact. What should you do?

You maintain an Azure AI Search index that grounds a RAG-based support agent. Documents are stored in an Azure Blob Storage container that receives roughly 50 updated or new files per day out of 2 million total blobs. The current indexer runs on a nightly schedule but reprocesses the entire container each run, consuming large amounts of enrichment (skillset) cost and taking hours. You need the indexer to process only blobs that have changed since the last successful run, with the least operational effort. What should you configure?

You build an Azure AI Search skillset that runs OCR, key phrase extraction, and a custom entity skill over a large collection of scanned contracts. Beyond serving a searchable index, your data science team wants the enriched, structured output (extracted entities and key phrases per document) persisted to Azure Blob Storage as tables and objects so they can run separate downstream analytics in Azure Machine Learning without re-invoking the skillset. What should you configure in the indexer/skillset to meet this requirement?

You are building an Azure AI Search index to support a RAG grounding pipeline over a large corpus of policy documents. Each document is chunked, and every chunk carries a text passage, a precomputed embedding vector, and a 'department' value used to scope queries. Requirements: users must be able to run hybrid (keyword + vector) queries, filter results by department, and see the original passage text returned in results, but the raw embedding vector should never be returned in the response payload to reduce bandwidth. How should you configure the field attributes in the index definition?

You are building an Azure AI Search ingestion pipeline for a repository of scanned PDF contracts. Each PDF contains both embedded digital text pages and image-only scanned pages. You must produce a single searchable text field per document that combines the native text with text recognized from the scanned images, so downstream RAG retrieval has complete grounding content. Which skillset configuration should you use in the indexer?

A logistics company uses an Azure AI Content Understanding custom analyzer to extract shipping fields (tracking number, weight, destination) from scanned bills of lading. Downstream, an agent auto-approves shipments based on the extracted values. Auditors flag that some low-quality scans produced incorrect destination values that were still auto-approved. You must ensure that uncertain extractions are routed to a human reviewer before the agent acts, without discarding well-extracted documents. What should you do?

You are building a RAG ingestion pipeline for a library of engineering white papers that contain descriptive charts and diagrams embedded within the text. Your grounding requirement is that when an agent answers a question about a chart, the retrieved chunk must include a natural-language description of what the chart depicts (trends, axes, labeled regions) rather than just the surrounding paragraph text, so the model can reason about the visual without receiving the raw image at query time. You are using Azure AI Content Understanding to produce the grounded representation. Which configuration produces the required output for downstream reasoning?

A legal-tech team uses Azure AI Content Understanding to extract clauses from scanned contracts and feed the results into a RAG agent. Downstream, compliance reviewers must be able to verify each extracted clause value by tracing it back to the exact location in the original document where it was found. Which capability of the Content Understanding analyzer output should the team rely on to satisfy this traceability requirement?

Your team is building a RAG pipeline for a legal firm. Source files are scanned contracts that combine paragraphs of prose, signature blocks, and embedded tables of payment schedules. Downstream, an agent must reason over both the narrative clauses and the tabular figures, and the retrieved chunks must preserve the reading order and table structure so the model does not confuse rows and columns. You are configuring an Azure AI Content Understanding analyzer to produce the grounded representation that will be chunked and indexed. Which analyzer output configuration best supports this requirement?

A logistics company processes three distinct document types (bills of lading, customs declarations, and delivery receipts) that arrive mixed together in a single intake queue. Each type has a different set of fields to extract, but all must be routed to the same downstream RAG grounding store as clean, structured output. You are building the ingestion stage with Azure AI Content Understanding. What is the most appropriate design to extract the correct fields per document type while minimizing misclassified extractions?

A financial services company stores thousands of recorded customer support calls as audio files. They need a pipeline that produces, for each call, a structured output containing the full transcript, a concise summary, the detected primary topic, and the customer's stated account issue category (chosen from a fixed list of categories) so these fields can ground an Azure AI Foundry agent. They want to minimize custom code and avoid chaining multiple separate services manually. Which approach best meets these requirements?

A logistics company ingests a nightly batch of scanned PDFs into a blob container. Each PDF may be an invoice, a bill of lading, or a customs declaration, and the document type is not indicated in the file name or metadata. You need each document routed to the correct Azure AI Content Understanding analyzer so that the appropriate field schema is applied, without manually pre-sorting the files. What should you configure in the pipeline?

A logistics company processes multi-page shipping manifests as scanned PDFs. Each page contains complex nested tables where cargo line items span multiple rows and are grouped under section headers. The data science team needs a clean, grounded representation that preserves the table structure (row/column relationships and section groupings) so a downstream RAG agent can reason accurately about which items belong to which shipment section. Which approach produces the most reliable structured representation for this requirement?

Your team is building a RAG solution over engineering maintenance manuals stored as PDFs. Each manual mixes narrative paragraphs, exploded-view diagrams with callout numbers, and specification tables. The downstream agent must reason over the text AND correctly associate diagram callout numbers with their referenced part descriptions in the tables. You need to produce a clean, grounded representation that preserves the reading order, table structure, and figure-to-text relationships in a single pass. Which approach best meets these requirements?

A financial services team uses Azure AI Content Understanding to extract line items from vendor invoices. They need the extracted vendor names and account codes validated against an authoritative master list of approved vendors so that the analyzer can flag entries that do not match a known record. Which capability should they configure to achieve this validation during extraction?

You are building a RAG ingestion pipeline in Azure AI Search for a corpus of long technical manuals (200+ pages each). Retrieval must return small, focused passages for precise grounding, but the agent needs enough surrounding context to reason accurately. During ingestion you must split each document into fine-grained chunks for embedding while still being able to hydrate the full parent section into the agent prompt at query time. Which indexing approach in the skillset should you configure?

You are building an Azure AI Search RAG ingestion pipeline for a library of long technical manuals. A skillset splits each manual into text chunks and generates vector embeddings per chunk. You need the index to store one document per chunk for fine-grained retrieval, while also preserving the parent manual's metadata (title, product line, revision date) so it can be returned as grounding context. Which indexer configuration achieves this?

You are building an Azure AI Search indexing pipeline to ground an agent on a corpus of scanned technical manuals stored as multi-page PDFs in Azure Blob Storage. Each page contains embedded diagrams and photographs alongside text. You need the skillset to run OCR on the page text AND separately analyze the embedded images with a custom vision skill, so both text and image-derived content can be merged into a single searchable field per document. Which indexer configuration is required so that the embedded images are extracted and made available as inputs to the skillset?

You are building an Azure AI Search index to ground a RAG application. Documents are chunked and embedded at ingestion using integrated vectorization backed by an Azure OpenAI embedding deployment. At query time, the application receives natural-language user questions as plain text and must retrieve the most semantically relevant chunks. You want to avoid writing custom code in the application to convert the incoming query text into a vector before searching. What should you configure so the search service embeds the query text automatically?

You are building an Azure AI Search vector index to ground a RAG agent. Documents are chunked and embedded using the text-embedding-3-large model at 3072 dimensions. During testing, indexing succeeds but every vector query returns an error and no results. You confirm the query vectors are generated from text-embedding-3-small. What is the most likely cause and correct fix?