AI-103
Free Microsoft Azure AI Apps & Agents Developer (AI-103) Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$1655 exam domainsLevel Intermediate2 timed practice tests in the course
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A company is building a customer support chatbot that must answer questions using their product documentation (500 PDF files, 2GB total). The bot needs to cite specific document sections in responses and handle 10,000 daily conversations with sub-2-second response times. The solution must minimize hallucination and provide verifiable source attribution. Which Azure AI Foundry approach BEST meets these requirements?
Azure AI Search vector indexing with RAG grounding is the correct pattern for knowledge-base scenarios requiring source attribution. This approach: (1) indexes documents in Azure AI Search with vector embeddings, (2) retrieves relevant chunks at query time, (3) grounds the LLM response in retrieved content, (4) automatically provides citations with document references. This is the standard Foundry pattern for minimizing hallucination while maintaining sub-2-second latency at scale.
A company is building a customer support application using Azure AI Foundry. The application must answer questions using the company's internal documentation stored in Azure Blob Storage, cite specific documents in responses, and handle follow-up questions while maintaining conversation context. Which Azure AI Foundry implementation approach BEST meets these requirements?
RAG (Retrieval-Augmented Generation) is the standard pattern for grounding LLM responses in specific document collections. Azure AI Search provides vector indexing and semantic search to retrieve relevant chunks, which are then passed as context to the LLM along with conversation history. This enables citations, handles token limits efficiently, maintains context across turns, and is the recommended Foundry pattern for knowledge-grounded applications.
A company is building an e-commerce platform that needs to generate product lifestyle images from text descriptions. The solution must support inpainting to remove unwanted objects from reference images and apply brand-specific watermarks to all generated images. Which Azure AI service configuration BEST meets these requirements?
Azure AI Foundry's image generation capabilities specifically support mask-based inpainting workflows for removing unwanted objects and provide built-in watermark injection features required for brand protection. This is the primary service for image generation and editing tasks in AI-103.
A healthcare application needs to extract patient symptoms, medications, and diagnoses from clinical notes while ensuring protected health information (PHI) is detected and redacted. The solution must work with Azure AI Foundry agents and provide structured JSON output. Which approach BEST meets these requirements?
Azure AI Foundry agents with structured output mode allow you to define a JSON schema and extract entities directly through generative prompting while built-in content safety filters detect PII/PHI. This provides an integrated solution meeting all requirements in a single agent configuration.
A financial services company needs to extract tables, handwritten signatures, and custom fields from 50,000 scanned loan documents stored in Azure Blob Storage. The extracted data must be indexed for semantic search and made available to an Azure AI agent for customer inquiries. Which solution BEST meets these requirements?
Azure AI Content Understanding (2026) provides multimodal document analysis combining OCR, layout analysis, and field extraction in a unified pipeline. It produces clean markdown or structured outputs optimized for RAG grounding. Azure AI Search with vector embeddings enables semantic/hybrid search, and registering the index as an agent tool provides the proper integration path for Azure AI agents to retrieve grounded information.
An AI developer is deploying a Foundry agent that uses three custom tools: one queries a SQL database, one calls an external weather API, and one sends approval emails. The agent will be used by 50 internal employees. Security policy requires that the agent cannot access the SQL database or send emails without explicit human approval for each action, but weather queries should execute automatically. Which Foundry agent configuration satisfies this requirement?
Foundry agents support per-tool approval workflows even in autonomous mode. The correct approach is to run the agent in autonomous mode (allowing self-directed tool execution) but attach approval workflow policies to specific tools (SQL and email). The weather tool executes without approval. This granular control is the intended Responsible AI pattern for tool-access governance in Foundry agents.
You are implementing a multi-agent orchestration solution in Azure AI Foundry where a coordinator agent delegates tasks to three specialist agents: one for SQL queries, one for data visualization, and one for report generation. The coordinator must track which specialist completed each subtask and handle failures by reassigning work. Which Foundry agent design pattern should you implement?
In Foundry multi-agent orchestration, the coordinator agent uses custom tools (functions) to invoke specialist agents. Each tool call represents delegating to a specialist, the response contains the result, and the coordinator maintains orchestration state (which specialist did what, success/failure) in its conversation context. This enables tracking, error handling, and reassignment logic within the agent framework.
A healthcare application needs to generate alt-text descriptions for medical images to ensure accessibility compliance. The solution must ground descriptions in actual visual evidence present in the image and flag any unsafe medical content. Which implementation approach is correct?
GPT-4o provides grounded visual understanding for generating evidence-based alt-text descriptions, and visual content filters in Azure Content Safety specifically detect unsafe visual content including medical imagery that violates policies. This combination addresses both accessibility and safety requirements.
A compliance team needs to analyze 50,000 customer support emails monthly to extract sentiment, detect regulatory concerns, and summarize key issues into 2-3 sentence summaries for each email. Processing must complete within 24 hours and stay under $500/month. Which solution provides the MOST cost-effective approach?
Batch processing with GPT-4o-mini using a single multi-task prompt minimizes costs by: 1) using the cheaper mini model, 2) processing in batch mode (50% discount), 3) one API call per email instead of three. A well-designed prompt can extract all three outputs in one pass, staying well under budget for 50K emails.
You are building a RAG ingestion pipeline for a legal document repository containing PDFs with complex multi-column layouts, embedded images, and footer annotations. The pipeline must preserve document structure for accurate agent grounding. Which Azure AI Search skillset configuration should you implement?
Document Layout skill (part of Azure AI Content Understanding) analyzes complex layouts including multi-column formats and preserves structural hierarchy. Following with OCR captures text including footer annotations, custom skill cleans formatting artifacts, Text Merge reconstructs proper document flow, and embedding with metadata preservation ensures accurate grounding with layout context retained.
A Foundry-based image analysis application processes user-uploaded photos using GPT-4o with vision capabilities. During testing, the system occasionally returns responses for images containing violence or graphic content that should be blocked. The development team needs to prevent the model from processing such images entirely. What is the MOST effective way to implement this safety control in Azure AI Foundry?
Azure AI Content Safety provides pre-processing image moderation that analyzes visual content for violence, hate, sexual content, and self-harm BEFORE images reach the generative model. Setting severity thresholds (e.g., block medium-high violence) prevents harmful images from being processed at all. This is the correct Responsible AI pattern—input filtering is more effective than relying on model safety guardrails alone.
An Azure AI Foundry application uses a deployed GPT-4 model to generate product descriptions. Evaluations show that 12% of outputs contain fabricated technical specifications not present in the source data. The application uses a RAG pattern with product specs retrieved from Azure AI Search. Which combination of techniques will MOST effectively reduce fabrication rate?
Self-critique (also called self-reflection or chain-of-verification) is an effective technique for reducing fabrication. The LLM generates a response, then explicitly verifies each claim against the retrieved context in a second reasoning step. If fabrications are detected, it regenerates. This leverages the model's reasoning capabilities and is a recommended Foundry pattern for improving factual grounding.
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How much does the AI-103 exam cost?
The exam fee is approximately $165 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 5 domains: Plan and manage an Azure AI solution (25-30%), Implement generative AI and agentic solutions (30-35%), Implement computer vision solutions (10-15%), Implement text analysis solutions (10-15%), Implement information extraction solutions (10-15%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $34.99 (list $109.99) through September 2 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.