AI-103: Azure AI App & Agent Developer Associate Exam Prep

Explore Microsoft Foundry, a unified AI workspace that integrates models, data, and deployment pipelines. Reduce friction, increase focus, and learn to build, test, and deploy AI apps in a workflow.

Discover the AI project lifecycle—develop, deploy, and monitor—through an iterative, continuous loop that builds, releases, observes, and improves AI solutions over time.

Explore the risks of API keys and compare them with Azure managed identities, revealing passwordless, identity-based authentication and when to use each for secure AI app development.

Master role-based access control for AI teams in Microsoft Foundry by defining roles, assigning scoped permissions, and avoiding common pitfalls to enable secure, collaborative workflows.

Select the right model by balancing performance, cost, and latency among GPT-5, Lama, and Phi-4, ensuring the solution fits the task and cost constraints.

Master tokens and rate limits in Azure to diagnose cost and performance issues in ai apps. See how usage and request rates affect cost, latency, and scalability for efficient deployments.

Learn to manage cloud costs for AI compute by setting alerts and monitoring usage with smart thresholds at 50%, 75%, and 90% to stay within budget.

Explore provisioning a multimodal endpoint that routes requests to multiple models behind a single entry point, balancing cost, performance, and accuracy with intelligent routing.

Compare Azure OpenAI service and Foundry models, highlighting direct API access versus a unified development environment, and guide when to choose speed and simplicity or structure and scalability.

Learn chain-of-thought prompting to guide AI reasoning step by step, improve accuracy, and decompose complex problems, with guidance on designing prompts and understanding limitations.

Connect your app to ai services using the Python sdk basics by covering setup, sending requests, handling responses, and building meaningful, interactive experiences.

Explore Whisper, a speech-to-text model, and connect voice input to AI apps via APIs for accurate transcription. Learn factors affecting accuracy and how to use transcripts for effortless voice interaction.

Master error handling by reducing hallucinations and API failures through mitigation, grounded data, structured prompts, validation, retries, fallbacks, and user-friendly messaging for reliable AI apps.

Explore how Azure AI Search uses vector indices to enable semantic search by converting text into vectors, indexing them, and querying by meaning.

Connect blob storage to Azure AI search through data ingestion, indexers, and indexing to power RAG-enabled AI apps with organized, up-to-date data and vector representations.

Discover how chunking, chunk size, and overlap affect retrieval accuracy and AI answers by using fixed, semantic, and structure-based strategies to preserve meaning.

Master vector embeddings with text-embedding-3-large to boost semantic search accuracy in ai apps. Learn embedding generation, storage, and hybrid search combining vectors and keywords.

Hybrid search combines keyword and vector results to improve relevance and accuracy. Learn how keyword search, vector search, ranking, and use cases drive robust retrieval and grounding.

Master grounding and citations to anchor AI responses in verified data, show sources, and build trust and transparency in retrieval-based AI systems.

Build a technical documentation chatbot using retrieval augmented generation (RAG) to search documents, retrieve relevant content, and generate cited answers. Ground responses with sources to ensure trust and concise explanations.

Discover how semantic kernel blends AI models with code to turn thinking into action, using plugins and planners to orchestrate real-world tasks.

Transform data between workflow nodes to ensure compatibility, manage data flow across steps, and apply parsing, mapping, filtering, and restructuring to keep AI pipelines reliable.

Build an automated email support agent that ingests emails, detects intent, routes to actions, and generates context-aware responses to speed up customer support.

Differentiate chatbots from AI agents by showing how AI agents perceive, decide, and act autonomously to complete tasks and achieve goals.

Explore autonomous agents and human-in-the-loop systems, examining when ai should act alone versus with human oversight, and balancing speed with safety in real-world applications.

Discover how a multi-agent system uses a manager agent to decompose tasks, delegate to specialized workers, and coordinate outputs through structured communication.

Navigate conflicts among multiple AI agents by detecting mismatches and applying rule-based, confidence-based, or consensus and validation strategies. Design production-ready AI systems that manage disagreement intelligently as a team.

Master state management by understanding memory types: short-term memory, long-term memory, and external knowledge, plus storage and context injection to enable production-ready, memory-enabled AI agents.

Build a production-grade multi-agent marketing system with three specialized agents: strategy, content, and creative, connecting via defined workflows, shared context, and validation for high-quality campaigns.

Learn how the Microsoft Responsible AI standard guides ethical, safe, fair, and trustworthy AI across the lifecycle, from development and evaluation to building a responsible AI culture.

Learn how to detect toxicity, configure filters for hate, violence, self-harm, and sexual content, apply input and output filtering, design safe responses, and monitor AI safety for real-world use.

Monitor ai systems in real time by tracking latency, throughput, and token usage to improve reliability and efficiency, then optimize prompts, scale infrastructure, and reduce costs.

Combine manual evaluation with automated checks to judge AI outputs by human evaluators, define clear criteria, and close the feedback loop for continuous improvement.

Learn to conduct fairness testing to detect bias in model output. Diagnose data and design bias, apply fairness metrics, and implement mitigation for responsible, ongoing ai.

Assess AI fairness by testing bias across inputs and measuring with fairness metrics. Mitigate bias with diverse data, clearer prompts, and human review, and maintain continuous monitoring for responsible AI.

Learn how to safely evolve AI in production through model versioning, blue-green deployment, and rollback strategies that enable tracking, testing, and risk-free releases.

Evaluate agentic apps across accuracy, reliability, safety, and performance with a structured mix of manual and automated tests, using real-world questions to drive continuous improvement.

Build an end-to-end enterprise agentic solution that perceives, reasons, and acts across systems with Azure OpenAI, tools, and memory. Ensure architecture, governance, evaluation, and safe, reliable deployment across workflows.

Design multi-step workflows by breaking tasks into connected steps with clear inputs and outputs, apply orchestration, maintain state, and handle errors for reliable end-to-end AI.

Master exam registration, slot selection, and online proctoring for the AI-103 exam. Learn system readiness, environment setup, and final tips to perform confidently on exam day.

Graduation marks the beginning of your career as an AI agent developer, designing digital employees that think, decide, and perform tasks, with projects and ongoing learning proving your value.