Microsoft GH-600 Exam Syllabus

Start Free GH-600 Exam Practice After Reviewing the Topics

Before starting your GH-600 exam preparation, it is recommended to review the complete Microsoft Developing in Agentic AI Systems exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free GH-600 questions. We also provide premium GH-600 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Microsoft

Vendor

GH-600

Exam Code

106

Total Questions

Total Exam Domains

Microsoft GH-600 Exam Objectives

Section 1: Prepare agent architecture and SDLC processes
Weight: 15-20%	Integrate agents into the software development lifecycle (SDLC) Identify steps for agents to perform Identify and mitigate common anti-patterns in agents Define inputs, outputs, and success criteria for agents Define boundaries between planning, reasoning, and action Configure agent planning to be distinct from agent execution Configure an agent to output a structured plan Validate agent plans Prevent agent action until the agent checked and approved Configure observability and control for autonomous agents Plan and implement the degree of agent autonomy, including guardrails Configure agent to produce inspectable artifacts within standard development tooling Configure human intervention for autonomous agents without slowing delivery
Section 2: Implement tool use and environment interaction
Weight: 20-25%	Select and configure agent tools Identify required tools Configure agent tools Configure agent tool permissions Configure MCP servers Add an MCP server as a tool to an agent Configure a GitHub remote MCP server Configure the MCP registries Configure MCP allow lists Integrate agents within development environments Evaluate the execution context for an agent Configure an agent's scope to a specific repository Configure an agent to be invoked in a CI workflow Configure an agent to use branch-based scope Enable an agent to perform autonomous actions, including creating branches and pull requests Configure an agent to handle environment-specific constraints Operate agents with safe execution paths and robust error handling Implement error handling Implement retries Implement rollbacks Implement escalation paths Implement traceability and accountability for agent actions
Section 3: Manage memory, state, and execution
Weight: 10-15%	Implement agent memory strategies Choose between short-term, long-term, and external memory Scope agent memory to task-relevant information Define memory expiration, pruning, and reset rules Persist agent state and manage context drift Capture task progress and decisions as durable artifacts Resume agent work without repeating steps or diverging from prior decisions Detect and correct drift during extended agent execution Ensure continuity of agent memory and state across tools and environments Share agent state Prevent conflicting context Prevent stale context
Section 4: Perform evaluation, error analysis, and tuning
Weight: 15-20%	Define success criteria and evaluation signals for agent tasks Specify expected outcomes and operational constraints for agent tasks Identify qualitative and quantitative evaluation signals to evaluate agents Align evaluation criteria with development intent Generate evaluation signals by using automated scanning tools Analyze agent failures and identify root causes Identify failures by using logs, plans, traces, outputs, and workflow artifacts Classify root causes, including reasoning errors, tool misuse, and context or environment issues Tune agent behavior based on evaluation results Revise instructions, workflows, or constraints Refine memory usage Refine tool usage and tool access
Section 5: Orchestrate multi-agent coordination
Weight: 15-20%	Operate and manage multi-agent workflows Apply an orchestration pattern to coordinate multiple agents Configure agent isolation for parallel execution Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs Configure observability for multi-agent behavior by using logs, artifacts, and operational signals Configure multi-agent workflows to produce artifacts suitable for review and audit Document key decisions, handoffs, and outcomes across agents Perform post-hoc analysis of multi-agent behavior Detect and respond to multi-agent failures and degraded behavior Identify failed, partial, or stalled agent executions Respond to degraded behavior or coordination across agents Implement multi-agent recovery patterns, including rollback and human-in-the-loop Manage the lifecycle of agents within multi-agent workflows Add agents to existing multi-agent workflows Update, reconfigure, or replace agents without disrupting active workflows Retire agents while preserving auditability and workflow continuity
Section 6: Implement guardrails and accountability
Weight: 10-15%	Define autonomy levels Classify agent actions by operational, security, and compliance risk to right-size human interventions Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards Implement guardrails and human-in-the-loop workflows Identify the subset of actions that require human judgment Block actions that violate defined security, compliance, or Responsible AI policies Scope permissions and execution contexts to enforce least-privilege access Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes Preserve execution velocity by minimizing approvals that do not materially reduce risk
Info	https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/gh-600

Microsoft GH-600 Exam Syllabus

Start Free GH-600 Exam Practice After Reviewing the Topics

START FREE GH-600 EXAM PRACTICE

Microsoft GH-600 Exam Objectives