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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Generate evaluation signals by using automated scanning tools 3. Align evaluation criteria with development intent 4. Specify expected outcomes and operational constraints for agent tasks - 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 2: Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checks and approves 2. Configure agent planning to be distinct from agent execution 3. Validate agent plans 4. Configure an agent to output a structured plan - 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 steps for agents to perform 2. Identify and mitigate common anti-patterns in agents 3. Define inputs, outputs, and success criteria for agents
Topic 3: Manage memory, state, and execution	10–15%	- Implement agent memory strategies 1. Scope agent memory to task-relevant information 2. Define memory expiration, pruning, and reset rules 3. Choose between short-term, long-term, and external memory - Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Resume agent work without repeating steps or diverging from prior decisions 3. Detect and correct drift during extended agent execution - Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent conflicting context 3. Prevent stale context
Topic 4: Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure MCP registries 2. Add an MCP server as a tool to an agent 3. Configure a GitHub remote MCP server 4. Configure MCP allow lists - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement rollbacks 5. Implement escalation paths - Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Configure an agent's scope to a specific repository 3. Configure an agent to handle environment-specific constraints 4. Evaluate the execution context for an agent 5. Enable an agent to perform autonomous actions, including creating branches and pull requests 6. Configure an agent to use branch-based scope - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools
Topic 5: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Scope permissions and execution contexts to enforce least-privilege access 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Identify the subset of actions that require human judgment - 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
Topic 6: Orchestrate multi-agent coordination	15–20%	- 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 - Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Identify failed, partial, or stalled agent executions 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - 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 - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Perform post-hoc analysis of multi-agent behavior 3. Configure multi-agent workflows to produce artifacts suitable for review and audit

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Microsoft Developing in Agentic AI Systems Sample Questions (Q37-Q42):

NEW QUESTION # 37
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Switch the agent to --allow-all mode
B. Add clear, reproducible steps and expected behavior to the issue
C. Increase MCP server rate limits
D. Enable Copilot memory

Answer: B

NEW QUESTION # 38
Your company uses GitHub Copilot Enterprise.
Developers use GitHub Copilot agent mode in Microsoft Visual Studio Code on their laptops and Copilot Chat on github.com when they are away from their laptops.
When switching between environments, the developers notice that agent workflows lose continuity because the tools available in Visual Studio Code are unavailable on github.com.
You need to ensure that the agent tools and state are available consistently across environments and can be used from any device without local setup.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements.

Answer:

Explanation:

NEW QUESTION # 39
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. copilot-instructions.md
B. A prompt file (.prompt.md)
C. agents.md
D. .copilotignore

Answer: B

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 # 40
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. Human-in-the-loop with required manual approvals for production
B. Read-only analysis with no ability to trigger workflows
C. Fully autonomous execution without approvals
D. Autonomous execution limited to non-production environments

Answer: A

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 # 41
You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

A. Ask mode
B. Plan mode
C. Edit mode
D. Agent mode

Answer: C

Explanation:
The correct answer is Edit mode. GitHub Copilot's Edit mode is designed for situations where the developer wants granular control over which files may be changed and what modifications should be applied. In Edit mode, you explicitly select the working set of files, provide natural-language instructions describing the required changes, and then review the proposed edits before accepting or discarding them. GitHub describes Edit mode as appropriate for quick, specific updates to a defined set of files and for scenarios where the developer wants tighter control over the editing process.
Agent mode differs because Copilot determines which files and tools are required, can execute terminal commands, and iterates autonomously toward completing the task. Ask mode is intended primarily for explanations, questions, and code suggestions rather than coordinated file modification. Plan mode generates an implementation strategy before execution and is appropriate when the approach must be reviewed before coding begins.
Therefore, where the requirement explicitly emphasizes selecting specific files and prescribing precise edits rather than delegating scope determination to the agent, Edit mode provides the correct level of developer control.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; selecting appropriate Copilot interaction modes; controlled code modification; human-directed versus autonomous execution.

NEW QUESTION # 42
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