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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q33-Q38):

NEW QUESTION # 33
You have a GitHub Enterprise Cloud Organization that uses a custom coding agent to run GitHub Actions workflows that create branches, open pull requests, and merge changes after required checks pass.
You need to log all agent-initiated actions and ensure that the logs are retained for two years.
What should you do?

A. Enable log forwarding.
B. Configure audit log retention.
C. Enable Git events in the audit log.
D. Configure audit log streaming to Azure Event Hubs.

Answer: D

Explanation:
To ensure all agent-initiated actions are preserved for two years, you must configure GitHub audit log streaming to an external log management platform or cloud storage provider.
By default, GitHub Enterprise Cloud only retains audit logs for 180 days and Git events for 7 days. Because the custom agent performs high-level platform modifications (such as branch creation, opening pull requests, and merges), native retention is insufficient. External streaming is the standard, secure way to enforce long-term compliance.
Reference:
https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise

NEW QUESTION # 34
During a Copilot CLI session, an MCP tool call fails because the external service requires re- authentication. What is the most likely resolution path?

A. Run /compact to clear context
B. Run /diff to inspect changes
C. Switch to plan mode
D. Re-authenticate the MCP server connector

Answer: D

Explanation:
MCP tool failures due to expired or invalid credentials require re-authenticating the connector itself; conversation-management commands like /compact or /diff don't address authentication issues.

NEW QUESTION # 35
Your organization requires that any workflow file changes proposed by an autonomous agent be reviewed by a member of the security team before merging, regardless of who opened the pull request. What combination should you configure?

A. copilot-instructions.md + required status checks
B. .copilotignore + agents.md
C. MCP server permissions + repository ruleset
D. CODEOWNERS + a required branch protection rule

Answer: D

Explanation:
A CODEOWNERS file designates the security team as required reviewers for the .github/workflows/* path, and a branch protection rule enforces that their approval is required before any pull request touching that path can merge.

NEW QUESTION # 36
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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:

NEW QUESTION # 37
You have a GitHub repository that uses the following GitHub Copilot CLI command in a Bash script.
#!/usr/bin/env bash
set -euo pipefail
PROMPT="Update dependencies, run tests, and open a pull request if changes are needed." copilot --autopilot --yolo --max-autopilot-continues 10 -p "$PROMPT" For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 38
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