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Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
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About the exam
Languages
Some exams are localized into other languages. You can find these in the Schedule Exam section of the Exam Details webpage. If the exam isn’t available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured
Audience profile
As a candidate for this Microsoft Certification, you’re an expert-level practitioner who has subject matter expertise in designing, building, and optimizing scalable, production‑ready, multi-agent AI systems and workflows. You manage the development from design to production.
You work closely with developers, machine learning engineers, platform engineers, data scientists, and business stakeholders to translate complex requirements into production-ready, multi-agent solutions.
For this exam, you should have experience developing AI and machine learning, deploying agentic systems in production environments, and orchestrating agent logic using Microsoft Foundry. You should also be proficient in Python.
Additionally, you need experience developing solutions that include Azure compute, network, storage, and data services. You should be familiar with open-source frameworks and standards, including Microsoft Agent Framework, Model Context Protocol (MCP), retrieval-augmented generation (RAG), and LangGraph.
Skills at a glance
Architect multi-agent solutions (15–20%)
Develop multi-agent solutions in Azure (30–35%)
Evaluate, optimize, and monitor multi-agent solutions (20–25%)
Secure, govern, and deploy multi-agent solutions (20–25%)
Architect multi-agent solutions (15–20%)
Design logical architecture for multi-agent solutions
Decompose goals and objectives into workflows, agents, and tools
Design workflows, including agents, subagents, control loops, and human-in-the-loop
Specify agent personas, scopes, boundaries, autonomy levels, and behavioral guidelines
Specify tool scopes, permission boundaries, and authentication methods
Specify appropriate protocols for communication, including between multiple agents and between agents and other components
Design controls and functionality to support human-AI experience (HAX) and to adhere to responsible AI principles
Design short-term and long-term memory architectures, including context sharing
Match task demands to model family capacities
Specify technology components to include in a multi-agent solution
Specify integration components to coordinate agent-to-agent communication, agent-to-tool interactions, and agent-to-knowledge-source routing across a multi-agent production system
Specify components for Zero Trust multi-agent solutions, including per-agent identity scoping, lateral movement prevention, and compliance control mapping for regulated deployments
Specify multi-tier state persistence components that address session state, shared team state, and long-term semantic memory with lifecycle and tenant-isolation policies
Specify compute components for multi-agent solutions that meet scalability, reliability, operational efficiency, security, and cost requirements
Specify observability components, including cross-service trace correlation, structured logging of agent reasoning paths, and agent replay capture for reproducible debugging
Specify monitoring components, including cross-agent coordination tracking, behavioral drift and quality regression detection, and automated remediation for agent failure patterns
Specify developer tools and environment to support the Software Development Lifecycle (SDLC), including dev containers, Visual Studio Code extensions, command-line interface (CLI), dependency management, and AI instructions
Develop multi-agent solutions in Azure (30–35%)
Design and implement advanced prompt engineering strategies in multi-agent solutions
Design and implement advanced prompt engineering techniques, including examples, dynamic context injection, defensive guidelines, and prompt lifecycle management
Design and implement context-aware multi-agent behaviors
Design and implement a strategy for fine-tuning agents and models, including data and frequency
Design and implement agent memory, context management, and knowledge integration
Design and implement context management in single agents and between agents, including context accumulation, retrieval, injection, and compaction
Design and implement single-agent and multi-agent memory strategies, including security, compliance, lifecycle, storage, and session management
Design and implement a multi-agent RAG architecture, including chunking, embedding quality, and retrieval precision
Design and implement knowledge integration for multi-agent consumption, including search, RAG, MCP-available sources, and semantic search
Build and integrate tool ecosystems
Integrate external resources, including function calling, specified tool use, and dynamic tool use
Design and build MCP servers and clients, including Azure Functions, Azure Logic Apps, and Azure API Management
Design tool error-handling and fallback mechanisms
Implement tool result validation and quality checks
Implement multi-agent orchestration
Implement multi-agent orchestration patterns, including hub-and-spoke, sequential, parallel, peer-to-peer, and orchestrator-subagent
Implement human-in-the-loop processes, including approval workflows, overrides, and edge case handling
Design and implement a caching strategy, including prompt caching, semantic caching, and response caching
Implement scaling and control of multi-agent spawning, batching, and concurrent execution
Integrate existing agents into a multi-agent solution securely by using Agent2Agent (A2A) and/or MCP
Implement multi-agent orchestration frameworks, including Agent Framework, LangChain, and LangGraph
Implement advanced multi-agent capabilities by using Hugging Face Transformers
Design middleware for reusable capabilities across the solution, including logging, authorization, and exception handling
Evaluate, optimize, and monitor multi-agent solutions (20–25%)
Design and implement evaluation and validation strategies for multi-agent solutions
Design and implement a human review process to evaluate solutions in Foundry
Design and implement evaluations for memory, knowledge, tools, and prompts
Optimize prompt and model performance for multi-agent solutions
Optimize task duration, including parallelism and rate limits
Diagnose context window issues, including sliding-window amnesia, summary drift, vector-only recall, and entity continuity issues
Design and implement continuous improvement, including large language model–as-a-judge (LLM-as-a-judge) frameworks, synthetic data generation, semantic optimization loops, and user feedback loops
Design and implement observability and monitoring for multi-agent solutions
Design a strategy for monitoring reliability, including agent health, workflow failures, trace correlation, drift detection, quality regression, and remediation
Design a strategy for monitoring solution and platform availability, performance, reliability, and service-level agreement adherence
Design and implement a strategy for optimizing token usage, including token limits, loop controls, and tool calls
Design a strategy for monitoring and managing cost, including usage, quotas, allocations, and chargebacks
Implement tracing in Foundry, including tokens, prompts, correlation IDs, alerting, and execution tracking
Secure, govern, and deploy multi-agent solutions (20–25%)
Design and implement security for multi-agent solutions
Specify and implement resource access, including identity-based access, network boundaries, access control policies, and role-based access control (RBAC)
Design and implement authentication flows in a multi-agent solution, including user impersonation, on-behalf-of, API keys, and OAuth 2.0
Design the secrets management and retrieval approach for multi-agent solutions by using Azure Key Vault, including secrets, certificates, key rotation, role-based access to secrets, and encryption
Implement shift-left security principles, including the AI Red Teaming Agent in Foundry
Design and implement guardrails for multi-agent solutions
Design and implement a multi-intervention guardrail strategy covering user inputs, tool calls, tool responses, and outputs
Design and implement custom guardrails for domain-specific constraints
Design guardrail testing and validation, including using synthetic data
Deploy multi-agent solutions to Azure
Choose a release methodology, including Development-Test-Acceptance-Production (DTAP), blue/green, and canary
Design and implement multi-environment release strategies, including rollback strategies, release management, and rollout
Design and implement a strategy for testing, including unit tests, regression tests, integration tests, and automated evaluations
Specify continuous integration and continuous deployment (CI/CD) requirements for integration and release, including infrastructure-as-code deployments and testing
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course |
| Find documentation | Microsoft Foundry documentation Build a multiple-agent workflow automation solution by using Microsoft Agent Framework Azure developer documentation |
| Ask a question | Microsoft Q&A | Microsoft Learn |
| Get community support | Microsoft Foundry Community Support Azure Community Support |
| Follow Microsoft Learn | Microsoft Learn - Microsoft Tech Community |
| Find a video | Exam Readiness Zone Azure Fridays Browse other Microsoft Learn shows |