Implement information extraction solutions (10–15%)

Microsoft Certified: Azure AI Apps and Agents Developer Associate · 27 questions

• A city permits office wants a grounded assistant that can use permitting PDFs, PNG diagrams, and training videos together. What should the retrieval pipeline do first?
• A county public-health bot must answer resident questions by meaning when wording differs from the published guidance titles. Which search approach should they configure for grounding?
• A transit authority needs residents to find both exact route numbers like 42B and natural-language questions about late-night service. Which search approach should they configure?
• A housing authority wants resident policy Q&A grounded in lease embeddings so paraphrased questions still retrieve the right clauses. Which search capability should they configure?
• A clerk’s office is ingesting scanned filing packets that mix text, images, and layout cues. What should the retrieval pipeline apply during indexing?
• A 311 team wants RAG answers about debris-pile complaints that arrive as handwritten photo attachments. What must their ingestion flow include?
• A library wants staff research agents to call the same retrieval pipeline used for the public catalog. How should they expose that pipeline?
• An emergency operations center needs search across audio situation reports and PDF annexes together. What should the index include?
• A water utility replaces its published tariff PDFs and needs the resident bot to stop citing outdated rates. What should the grounding team do to the search index?
• A parks department wants event dates pulled out during enrichment so the search index can filter programs by calendar. What should they add to the ingestion pipeline?
• An open-data portal wants a workflow that drafts dataset descriptions using evidence from related documentation. How should retrieval participate?
• A courts research desk must match exact case citation strings and still support semantic questions about holdings. Why prefer hybrid search over vector-only?
• Public works field agents need RAG answers from handwritten notes on as-built drawing sheets. What should ingestion add to the corpus pipeline?
• The mayor’s office wants press FAQ answers grounded only in approved briefing books, not informal staff drafts. How should retrieval be scoped?
• A clerk must pull structured fields from multimodal permit applications that mix typed text, stamps, and form layout. Which pipeline approach fits?
• A housing authority needs clean grounded document representations of lease PDFs before agents and RAG consume them. Which Foundry capability should they use?
• Transit wants fare-policy PDFs turned into markdown tables that downstream reasoning can consume cleanly. What should their document analyzers output?
• A 311 intake flow must capture checkbox selections and handwritten regions on scanned service forms. What should extraction target?
• Public health receives immunization-clinic packets that mix born-digital PDFs with scanned pages and still needs structured clinic fields. What should the extraction design support?
• A library funding agent needs grant PDF narratives as markdown so it can reason over sections cleanly. What should document analyzers produce?
• Emergency management needs section-aware extraction from thick Emergency Operations Plan PDFs so annexes and chapters stay distinct. Which document capability should they apply?
• A water utility must extract dispute evidence from bill packets that include charts and complex layouts, not just plain paragraphs. How should they choose the extraction approach?
• A courts research agent must not answer from raw unclean OCR alone. What should the document pipeline produce first?
• A parks AP review agent needs structured vendor fields such as amounts and invoice numbers from PDF invoices. What should the extraction step provide?
• An open-data team wants methodology sections from statistical report PDFs turned into structured outputs for cataloging. What should analyzers produce?
• A county assessor needs deed-related scans searchable by extracted fields after OCR. Which pipeline combination should they run?
• A city attorney’s office is building a clause-finding agent over contract PDFs. How should Content Understanding analyzers support that agent?