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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q29-Q34):

NEW QUESTION # 29
You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 30
You need to enable Copilot memory to support the planned changes.
What should you configure?

A. The Copilot settings of each repository
B. The Copilot settings of the organization
C. The agent profile of agent1
D. The personal Copilot settings of each developer

Answer: B

Explanation:
Copilot memory is an organization-level capability. Configuring it through the organization's Copilot settings establishes the governance boundary, availability, and policy controls consistently for the developers and repositories covered by that organization.
Personal settings are unsuitable because they create inconsistent behavior between developers and do not provide centralized administration. A repository-level configuration can supply repository instructions and scoped context, but it does not replace the organization-level control that enables and governs the memory capability. An individual agent profile defines role behavior, tool access, prompts, and delegation settings; it is not the tenant-level location for enabling Copilot memory.
Organization configuration is particularly important when agents must retain approved context, recurring pReference, and workflow guidance across sessions without each developer configuring behavior independently. Central governance also allows administrators to maintain predictable controls over which organizational information is available to Copilot.
Study-guide topics: organizational governance, persistent context, and centralized agent configuration.

NEW QUESTION # 31
An agentic coding session in the terminal has grown too long, and you want to discard all prior conversation history and start clean, without summarization. Which slash command should you run?

A. /usage
B. /reset-context
C. /compact
D. /clear

Answer: D

Explanation:
/clear wipes the entire conversation history and starts a fresh session. This differs from /compact, which summarizes history to save tokens while retaining prior progress.

NEW QUESTION # 32
You have a GitHub Enterprise Cloud organization that uses GitHub Actions for CI/CD.
You plan to enable an agent to update and deploy workflows that access production environment secrets.
You need to select an autonomy level for a compliance-sensitive workflow.
Which autonomy level should you select?

A. Read-only analysis with no ability to trigger workflows
B. Fully autonomous execution without approvals
C. Human-in-the-loop with required manual approvals for production
D. Autonomous execution limited to non-production environments

Answer: C

Explanation:
Human-in-the-loop execution provides the appropriate autonomy level because the agent's actions affect production deployment and access to production secrets. The agent can prepare changes and perform permitted validation, while an authorized person approves the production transition. This retains useful automation while placing human judgment at the consequential execution boundary.
GitHub Actions environments support required reviewers. A job referencing a protected environment must satisfy its protection rules before proceeding. GitHub explicitly documents that when approval is required, the job cannot access environment secrets until a required reviewer approves it. The approval gate therefore controls an operational capability, rather than merely asking the model to behave cautiously.
Option A removes the oversight required by the scenario. Option B prevents the agent from performing the requested deployment work. Option D restricts execution to non-production environments and consequently does not provide a controlled route to production.
The workflow should bind deployment to the protected production environment and align reviewer permissions with organizational policy. Relevant curriculum topics are risk-based autonomy levels, explicit authorization for compliance-sensitive changes, and least-privilege execution.
Reference:

NEW QUESTION # 33
Drag and Drop Question
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent.
You need to ensure that the default GITHUB_TOKEN permissions are read-only, and write access is granted to only the job that performs repository write operations. The workflow must be able to create and approve pull requests only when explicitly enabled.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
How should you complete the workflow?
Box 1: contents: read
Set Global Read-Only Permissions
To enforce the principle of least privilege, the top-level permissions block must strip all default write capabilities from the GITHUB_TOKEN. Setting contents: read allows the workflow to fetch the code but prevents any accidental repository modifications.
Box 2: contents: write
Grant Job-Level Write Access
Only the specific job responsible for modifying the repository should receive write access. The update_artifacts job requires permission to push code modifications back to the repository.
Box 3: pull-requests: write
To successfully create and approve pull requests, you must explicitly enable this capability in your GitHub repository settings:
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/disabling-or-limiting-github-actions-for-your-organization

NEW QUESTION # 34
......

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