Microsoft Certified Azure AI Apps and Agents Developer Associate (AI-103) Practice Exam
Microsoft Certified Azure AI Apps and Agents Developer Associate (AI-103) Practice Exam
About Microsoft Certified Azure AI Apps and Agents Developer Associate (AI-103) Exam
The Microsoft Certified Azure AI Apps and Agents Developer Associate (AI-103) Practice Exam is designed for developers and AI professionals who want to build intelligent AI-powered applications and autonomous AI agents using Microsoft Azure and Microsoft Foundry. This certification focuses on modern Generative AI development, agentic AI workflows, multimodal AI applications, and enterprise-grade AI solution deployment.
The Microsoft Azure AI Apps and Agents Developer Associate certification validates your ability to design, build, deploy, secure, and manage AI applications and intelligent agents using Azure AI technologies. The certification focuses heavily on Generative AI, AI agents, Azure AI Foundry, AI orchestration, prompt engineering, vector search, and responsible AI implementation.
Who should take this Exam?
This certification is suitable for professionals interested in modern AI applications and agent development.
- AI developers and software engineers
- Azure AI engineers
- Cloud developers and architects
- Generative AI professionals
- Application developers exploring AI integration
- Professionals building AI copilots and AI agents
- Developers working with LLMs and RAG systems
Career Opportunities
The AI-103 certification can help professionals explore advanced AI and cloud development roles focused on Generative AI and intelligent application development.
- Azure AI Developer
- Generative AI Engineer
- AI Application Developer
- AI Agent Developer
- AI Solutions Architect
- AI Integration Engineer
- Cloud AI Engineer
- Prompt Engineer
- Conversational AI Developer
What is this exam?
The AI-103 certification represents the next generation of Azure AI certifications, moving beyond traditional AI APIs toward intelligent AI applications capable of reasoning, planning, tool usage, memory integration, and multi-agent orchestration. It is ideal for professionals who want to work with Large Language Models (LLMs), AI copilots, Retrieval-Augmented Generation (RAG), Azure AI Foundry, and AI-powered enterprise applications.
Skills Required
The AI-103 certification is intended for learners with development and cloud fundamentals. Candidates preparing for this exam should ideally have:
- Basic programming knowledge in Python or C#
- Familiarity with APIs and SDKs
- Understanding of Azure fundamentals
- Knowledge of cloud application development
- Basic AI and machine learning concepts
- Understanding of REST APIs and JSON
Prior experience with Generative AI or Azure AI services can be beneficial, but is not mandatory for beginners transitioning into AI application development.
Knowledge Gained
After completing the AI-103 preparation, learners will gain practical knowledge of designing and managing AI-powered applications and intelligent agents within enterprise environments.
- End-to-end AI application development workflows
- Generative AI solution architecture
- AI agent design and orchestration
- Azure AI Foundry services and deployment models
- Prompt engineering and grounding techniques
- Vector search and Retrieval-Augmented Generation (RAG)
- Multimodal AI processing
- AI safety, monitoring, and governance
- AI model deployment and lifecycle management
- Enterprise AI integration strategies
Course Outline
The Microsoft Certified Azure AI Apps and Agents Developer Associate (AI-103) Exam covers the following topics -
Domain 1 - Describe how to plan and manage an Azure AI solution (25–30%)
1.1 Explain the appropriate Foundry services for generative AI and agents
- Learn to choose an appropriate model for each task, including large language models (LLMs), small language models, multimodal models, and Foundry Tools
- Learn to choose the appropriate Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing
- Learn to choose an appropriate method for retrieval and indexing
- Learn to choose appropriate memory, tools, and knowledge integration services for agent solutions
1.2 Explain how to set up AI solutions in Foundry
- Learn to design Azure infrastructure for AI apps and agent-based solutions
- Learn to choose appropriate deployment options
- Learn to configure model and agent deployments
- Learn to integrate Foundry projects with continuous integration and continuous deployment (CI/CD) pipelines
1.3 Explain how to manage, monitor, and secure AI systems
- Learn to manage quotas, scaling, rate limits, and cost footprints for model and agent workloads
- Learn to monitor model performance, drift, safety events, and grounding quality
- Learn to monitor data ingestion quality, search index health, and relevance performance
- Learn to configure security, including managed identity, private networking, keyless credentials, and role policies
1.4 Explain how to implement responsible AI across generative AI and agentic systems
- Learn to configure safety filters, guardrails, risk detection, and content moderation
- Learn to apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling
- Learn to implement auditing through trace logging, provenance metadata, and approval workflows
- Learn to govern agent behavior with oversight modes, constraints, and tool-access controls
Domain 2 - Describe how to implement generative AI and agentic solutions (30–35%)
2.1 Explain how to build generative applications by using Foundry
- Learn to deploy and consume LLMs, small models, code models, and multimodal models
- Learn to implement retrieval-augmented generation (RAG) in an application
- Learn to design workflows, tool-augmented flows, and multistep reasoning pipelines
- Learn to evaluate models and apps, including detecting fabrications, relevance, quality, and safety
- Learn to integrate generative workflows into applications by using Foundry SDKs and connectors
- Learn to configure an application to connect to a Foundry project
2.2 Explain how to build agents by using Foundry
- Learn to define agent roles, goals, conversation-tracking approach, and tool schemas
- Learn to build agents that integrate retrieval, function-calling, and conversation memory
- Learn to integrate agent tools, including APIs, knowledge stores, search, content understanding, and custom functions
- Learn to implement orchestrated multi-agent solutions
- Learn to build autonomous or semiautonomous workflows with safeguards and approval flow controls
- Learn to integrate monitoring into deployed agents, evaluate agent behavior, and perform error analysis
2.3 Explain how to optimize and operationalize generative AI systems
- Learn to tune generation behavior, such as prompt engineering and adjusting model parameters
- Learn to implement model reflection, chain-of-thought evaluations, and self-critique loops
- Learn to set up observability by implementing tracing, token analytics, safety signals, and latency breakdowns
- Learn to orchestrate multiple models, flows, or hybrid LLM and rules engines
Domain 3 - Implement computer vision solutions (10–15%)
3.1 Explain how to design and implement image- and video-generation solutions
- Learn to implement a solution that generates images from text prompts and reference media
- Learn to implement a solution that generates videos from text prompts and reference media
- Learn to configure image-editing workflows, including inpainting, mask‑based edits, and prompt‑driven modifications
- Learn to implement workflows to edit generated videos
- Learn to select and apply appropriate generation and editing controls provided by the platform
3.2 Explain how to design and implement multimodal understanding workflows
- Learn to build a solution that analyzes visual context by using multimodal models
- Learn to configure apps to produce concise or detailed captions for single or multiple images
- Learn to implement a solution that enables question‑answering grounded in visual evidence
- Learn to configure generation of alt‑text and extended image descriptions aligned to accessibility guidelines
- Learn to implement visual understanding by configuring Azure Content Understanding in Foundry Tools to extract visual characteristics
- Learn to implement video analysis workflows to process and interpret video segments
- Learn to configure single‑task and pro‑mode Content Understanding pipelines
- Learn to implement solutions that identify objects, components, or regions within images or video
3.3 Explain how to implement responsible AI for multimodal content
- Learn to implement filters to classify unsafe or disallowed visual content
- Learn to detect and mitigate indirect prompt injection by using embedded text in images
- Learn to enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content
Domain 4 - Implement text analysis solutions (10–15%)
4.1 Explain how to apply language model text analysis
- Learn to implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools
- Learn to configure detection of sentiment, tone, safety issues, and sensitive content
- Learn to build solutions that translate text by using Azure Translator in Foundry Tools or LLM‑powered translation flows
- Learn to customize language model outputs for domain tasks, such as compliance summarization and domain extraction
4.2 Explain how to implement speech solutions
- Learn to implement workflows to convert speech to text and text to speech for agentic interactions
- Learn to integrate speech as an agent modality, including custom speech models
- Learn to enable multimodal reasoning from audio inputs
- Learn to translate speech into other languages by using language models and Foundry Tools
Domain 5 - Exokain how to implement information extraction solutions (10–15%)
5.1 Explain how to build retrieval and grounding pipelines
- Learn to ingest and index content, such as documents, images, audio, and video
- Learn to configure semantic search, hybrid search, and vector search for grounding
- Learn to implement enrichment by using custom or built-in skills for text, images, and layout
- Learn to configure RAG ingestion flow, including documents and using optical character recognition (OCR)
- Learn to connect retrieval pipelines directly to workflows and agent too
What will you learn?
- Building Generative AI applications using Azure AI Foundry
- Developing AI agents and agentic workflows
- Working with Large Language Models (LLMs)
- Prompt engineering and AI orchestration techniques
- Retrieval-Augmented Generation (RAG) implementation
- AI agent memory and tool integration
- Azure AI Search and vector databases
- Multimodal AI applications and content processing
- Responsible AI and AI governance practices
- Monitoring, securing, and deploying AI solutions on Azure
