Medium AI-103 practice questions

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 retail team uses an Azure OpenAI image-generation model to update product marketing images. They need to replace only the background of an existing photograph while keeping the product itself pixel-identical, and they must specify exactly which region should be regenerated. Which image-editing capability should they use?

A legal-tech company processes thousands of lengthy contract documents overnight. They need each document condensed into a concise, fluent narrative overview that rephrases key clauses in natural language for busy executives, rather than pulling verbatim sentences. They want to use Azure AI Language's summarization capability. Which summarization approach should they configure?

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?

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 retail company wants to automate ingestion of supplier product photos. For each image, they need a structured JSON record containing the dominant color, whether packaging is present, and a short marketing description, using a schema they define once and apply across thousands of images. Which Azure capability should the developer use to define custom fields and extract these structured visual characteristics per image?

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?

You are building a voice-enabled agent for a hospital's radiology department. During testing, the standard Azure AI Speech-to-text model frequently mis-transcribes specialized medical terms (e.g., 'oligodendroglioma', 'cholecystectomy') that clinicians dictate, even though general conversational speech transcribes accurately. You have a corpus of correctly spelled domain terms and representative sentences but only a small amount of matching audio. Which approach best improves recognition accuracy for these terms?

A support platform team is building an agent that reviews written customer chat transcripts. They need to detect whether messages express negative sentiment AND identify specific opinion targets (e.g., 'the checkout was slow' should link the negative opinion to 'checkout'). They want a managed Azure AI Language capability rather than crafting their own generative prompt, to keep results consistent and low-cost at high volume. Which capability should they use?

A media company uses an Azure OpenAI image-generation deployment (gpt-image) to let marketing staff create promotional artwork from free-text prompts. Compliance requires that the pipeline automatically block any generated image containing sexual, violent, or hateful visual content before it is delivered to the end user, without the developer writing custom classifier code. Which approach best meets this requirement?

A marketing team uses an image-generation model in Azure AI Foundry to produce promotional assets. Compliance requires that every published image include the company watermark and must never contain competitor logos or other prohibited brand symbols. The team wants an automated post-generation step that inspects each produced image, verifies the presence of the required watermark, and flags any prohibited brand marks before the asset is released. Which approach best enforces these visual policy rules?

A product team collected 40,000 free-text customer feedback comments with no predefined categories. They want to discover the recurring themes across the corpus—without maintaining a fixed label list—so they can prioritize product improvements. Which Azure AI Language capability best fits this requirement?

You are building a semiautonomous customer-support agent in Azure AI Foundry. The agent can call a custom function that issues refunds directly to customer payment methods. Business rules require that any refund over $500 be reviewed and explicitly approved by a human support supervisor before execution, while smaller refunds may proceed automatically. You need to implement this with the least custom orchestration code while keeping an auditable record of decisions. What should you do?

You are building a customer-support agent in Azure AI Foundry Agent Service. The agent answers product questions from an internal knowledge base, but users also ask about breaking news that affects your products (e.g., recent regulatory changes and current market prices) that changes daily and is not present in your indexed documents. You must add a capability so the agent can retrieve up-to-date public web information at query time while keeping your existing knowledge-base retrieval for internal content. Which tool should you add to the agent?

You are building an Azure AI Foundry agent for a finance team. Users upload CSV files containing quarterly sales data and ask the agent to compute custom aggregations, generate correlation statistics, and produce chart images on demand. The requested calculations vary unpredictably per user and cannot be pre-defined as fixed functions. Which built-in agent tool should you enable to satisfy these requirements?

Your team is building a generative application in Azure AI Foundry that summarizes long legal contracts. Business stakeholders require the highest possible summarization quality and are willing to accept higher per-token cost and slower response times, since summaries run as scheduled overnight batch jobs rather than interactively. You must choose a deployment approach in Foundry that best fits this workload. Which approach should you select?