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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q10-Q15):

NEW QUESTION # 10
You have a GitHub Copilot coding agent named Orchestrator that runs a multi-phase workflow by using the following subagents:
Explorer gathers context by using read-only tools.
Modifier applies focused edits.
You are adding a new agent named Summarizer that generates a concise summary after modifications are complete. Summarizer includes the following YAML frontmatter:
---
name: Summarizer
description: Produce a concise summary of recent changes
tools: ['fetch']
user-invocable: false
disable-model-invocation: true
---
The Orchestrator agent lists all three agents in its agents property.
After adding the Summarizer agent, Orchestrator successfully runs Explorer and Modifier but fails to run Summarizer.
What is a possible cause of the failure?

A. Summarizer cannot be invoked as a subagent because disable-model-invocation is set to true.
B. Orchestrator is missing a handoff entry to trigger Summarizer.
C. Summarizer is missing the editing tools required to complete the workflow.
D. Orchestrator cannot call Summarizer because user-invocable is set to false.

Answer: B

Explanation:
D is the best answer among the available choices. The key detail is that Summarizer is explicitly listed in Orchestrator's agents property. Current VS Code agent orchestration behavior states that explicitly listing a custom agent in the parent's agents array overrides disable-model-invocation: true for that coordinator. Therefore, option A does not explain the failure in this scenario.
Likewise, user-invocable: false does not prohibit programmatic or subagent invocation. It merely prevents users from manually selecting that agent from the agent picker; this setting is commonly used specifically for worker agents that should only operate as subagents.
Option C is also incorrect because Summarizer's function is to produce a summary, not modify repository content. It therefore does not inherently require editing tools.
The agents property establishes which subagents the coordinator is permitted to invoke; it does not itself define the sequence of workflow transitions. Handoffs are the mechanism for defining explicit guided transitions between agents. If the workflow expects Summarizer to run as the next defined phase but no corresponding transition/invocation exists, the missing handoff can explain why execution stops after Modifier.
Study Guide Reference Topics: Orchestrate Multi-Agent Coordination; custom subagents; agents restrictions; agent invocation controls; sequential handoffs and workflow transitions.

NEW QUESTION # 11
You have a GitHub repository that contains an agent named Orchestrator. Orchestrator delegates work to the following specialized subagents:
* Planner reviews issues and creates a plan of action.
* Implementer writes code based on the plan of action.
* Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
B. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
C. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.

Answer: B

Explanation:
The configuration must be changed on the agent that performs the delegation. Orchestrator is responsible for coordinating the workflow, so its allowed subagent list must include Summarizer. Adding the new agent to that list makes it an available delegation target alongside Planner, Implementer, and Reviewer.
An agent's agents property identifies the custom agents it can invoke as subagents. The tools property serves a different purpose: it identifies available capabilities such as reading files, editing code, or invoking agents.
Adding a custom agent's name to the tools list does not register that agent as a callable subagent. The agent invocation tool must also be available; the scenario already establishes that Orchestrator delegates to other subagents.
Changing Reviewer's configuration would enable a relationship originating from Reviewer, rather than the requested direct invocation by Orchestrator. A handoff also represents a different interaction pattern from having the orchestrating agent invoke a subagent and receive its result.
Availability does not force execution on every task. Orchestrator's instructions should indicate when a summary is required and what information it should contain.
Study-guide topics: delegation permissions, orchestrator responsibilities, and specialized subagents.
Reference: VS Code-Subagents .

NEW QUESTION # 12
You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?

A. agents.md
B. .copilotignore
C. A prompt file (.prompt.md)
D. copilot-instructions.md

Answer: C

Explanation:
Prompt files define reusable, invocable prompt templates for specific tasks or personas. Unlike copilot-instructions.md, which applies globally and automatically, prompt files are opt-in and triggered explicitly.

NEW QUESTION # 13
During an agentic session, you want to see exactly how many tokens are being consumed by the system prompt, tools, and message history before deciding whether to compact. Which command should you run?

A. /usage
B. /diff
C. /context
D. /tokens

Answer: C

Explanation:
/context displays a breakdown of the active context window, including tokens used by the system prompt, MCP/system tools, and message history, along with remaining free space.

NEW QUESTION # 14
You are analyzing the following agent logs.

You need to classify the error in a report for your company's CTO.
How should you classify the error?

A. Network issue
B. Reasoning error
C. Context Issue
D. Tool misuse

Answer: D

Explanation:
This is a Tool misuse error (specifically, a configuration/implementation bug).The agent successfully passed the reasoning and context phases, but the environment's internal handler failed because the mcp_microsoftdocs_microsoftdocs_search tool was not properly registered or the IDE failed to process the tool call's schema.
Reference:
https://github.com/anthropics/claude-quickstarts/issues/345

NEW QUESTION # 15
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