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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Perform evaluation, error analysis, and tuning	15-20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints - Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents - 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
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 - 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 - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity - 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
Implement guardrails and accountability	10-15%	- Implement guardrails and human-in-the-loop workflows 1. Block actions that violate defined security, compliance, or Responsible AI policies 2. Identify the subset of actions that require human judgment 3. Scope permissions and execution contexts to enforce least-privilege access 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 - 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
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. Detect and correct drift during extended agent execution 2. Capture task progress and decisions as durable artifacts 3. Resume agent work without repeating steps or diverging from prior decisions - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Share agent state 3. Prevent stale context
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 be invoked in a CI workflow 4. Evaluate the execution context for an agent 5. Configure an agent to use branch-based scope 6. Configure an agent to handle environment-specific constraints - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement rollbacks 3. Implement retries 4. Implement escalation paths 5. Implement traceability and accountability for agent actions - Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Configure MCP servers 1. Configure MCP allow lists 2. Add an MCP server as a tool to an agent 3. Configure the MCP registries 4. Configure a GitHub remote MCP server
Prepare agent architecture and SDLC processes	15-20%	- Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Identify steps for agents to perform 3. Define inputs, outputs, and success criteria for agents - Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checked and approved 2. Configure an agent to output a structured plan 3. Configure agent planning to be distinct from agent execution 4. Validate agent plans - Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails

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Microsoft Developing in Agentic AI Systems Sample Questions (Q91-Q96):

NEW QUESTION # 91
Hotspot Question
Your company uses Microsoft Visual Studio Code and GitHub Copilot Chat.
You have a GitHub repository that uses main as the default branch. The repository contains a workspace custom agent stored at .github/agents/release-notes.agent.md.
A developer switches to a branch named branch1 where the agent file does NOT exist. In the same session, the developer switches to a user profile named profile1.profile1 contains a custom agent file named release-notes.agent.md that has user-invokable set to false.
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:

Explanation:
Box 1: No
The agent will not appear in the agents dropdown with a display name of release-notes because the developer is currently on branch1 and profile1, which effectively hides it.
Box 2: No
The workspace version of the release-notes agent will not be available in the agents dropdown when the developer is on branch2.
Box 3: No
The user profile agent will not appear in the agents dropdown, nor will it override a workspace agent in this scenario.
Reference:
https://code.visualstudio.com/docs/agent-customization/custom-agents

NEW QUESTION # 92
You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

A. /compact
B. /plan
C. /context
D. /diff

Answer: D

Explanation:
The correct command is /diff. GitHub Copilot CLI provides /diff specifically for reviewing code changes directly from the terminal. GitHub's current CLI command reference defines /diff as a command that reviews changes in the current directory and can automatically switch to a branch comparison when the working tree is clean. This makes it the appropriate command when validating an agent-generated change set before accepting or merging the work.
The other commands serve different purposes. /context displays context-window utilization, including token allocation and remaining context capacity; it does not show code modifications. /compact summarizes conversation history to reclaim context-window space during long-running sessions. /plan creates an implementation plan before coding and restricts normal project-file modifications while the plan is being prepared.
Using /diff supports an essential human-in-the-loop control in agentic software development: generated code should be inspected before it crosses a merge or deployment boundary. Reviewing the diff exposes additions, deletions, and modifications so that unintended or unsafe changes can be identified before integration.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; agent-generated code review; human-in-the-loop validation; controlled execution and change inspection.

NEW QUESTION # 93
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
* agent-run that generates trace.json and plan.md
* review that waits for human approval before continuing
* deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?

A. Use dependency caching to store trace.json and plan.md.
B. Commit trace.json and plan.md back to the repository from agent-run.
C. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
D. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.

Answer: C

Explanation:
Workflow artifacts provide the required combination of retained execution evidence and file transfer between jobs. The agent-run job uploads trace.json and plan.md, associating both files with the workflow run.
Reviewers can access the resulting artifact through the run's GitHub Actions interface, while downstream jobs download it to their own execution environments.
This is necessary because separate jobs do not automatically share a local filesystem. Declaring a dependency establishes execution order but does not transfer the generated files. The review and deployment stages therefore need explicit artifact downloads in addition to their appropriate job dependencies.
Committing transient traces and plans introduces repository changes solely to transport run-specific evidence.
A network share adds external infrastructure and does not directly satisfy the requirement for standard Actions artifact access. Dependency caching is intended for reusable inputs such as downloaded dependencies; it is not the appropriate mechanism for authoritative outputs from a particular agent run.
Artifacts preserve the plan and trace that informed the review, allowing subsequent deployment activity to use the corresponding files. Relevant curriculum topics are inspectable agent artifacts, observability, and human intervention.
Reference: Store and share data with workflow artifacts .

NEW QUESTION # 94
You need to configure agent1 to support the planned changes.
What should you do?

A. In the agent configuration, replace line 05 with the following: tools: []
B. Add Use all available tools to the .github/copilot-instructions.md file.
C. Delete line 05 from the agent configuration.
D. Add Use all available tools to the instructions in the agent configuration.
E. Add the mcp-servers property to the agent configuration.

Answer: E

Explanation:
The mcp-servers property connects a custom agent to the Model Context Protocol servers required for its planned work. MCP configuration makes defined external tools and services available to the agent in a controlled, declarative manner.
Adding a broad natural-language instruction such as "Use all available tools" does not configure a server connection, authenticate a tool, or define which external capabilities are available. It is also poor least- privilege design because it encourages unrestricted tool use without establishing a technical permission boundary.
Setting tools: [] removes all configured tools and would prevent the agent from using required capabilities.
Deleting the tools line could cause the agent to inherit or expose a broader default tool set than intended.
Repository-wide instructions can guide behavior, but they do not replace the agent-specific configuration needed to associate MCP servers with that agent.
A well-designed configuration identifies only the MCP servers necessary for the role, supplies secrets through approved environment mechanisms, and limits the resulting tools to the agent's defined task.
Study-guide topics: MCP integration, agent tool configuration, and least-privilege environment access.

NEW QUESTION # 95
You have a GitHub Actions workflow that runs a multi-agent job. Each agent uploads its output as a workflow artifact.
A completed workflow run produces unexpected code changes, and the job logs do NOT show the agents' intermediate outputs.
You need to retrieve the agents' captured outputs from the completed run for post-hoc analysis.
What should you do in GitHub Actions?

A. Download the workflow artifacts from the run summary page.
B. Enable step-level masking for secrets in the workflow.
C. Review the repository's commit history for the run.
D. Rerun the workflow with debug logging enabled.

Answer: A

Explanation:
The captured outputs were uploaded as workflow artifacts, so the correct action is to download those artifacts from the completed run's summary page. This retrieves the evidence produced during the execution that generated the unexpected changes.
Workflow artifacts and job logs contain different information. Logs record output emitted during workflow execution, while artifacts preserve files explicitly uploaded by workflow steps. An agent can therefore produce an intermediate report, proposed patch, or structured result that exists in an artifact even when its contents were never printed to the job log.
Rerunning with debug logging creates a new execution. Because agent behavior can vary with context, model responses, and repository state, a later run cannot substitute for examining the original captured outputs.
Secret masking controls disclosure rather than evidence retrieval. Commit history shows committed changes but does not necessarily contain intermediate agent reasoning or generated files excluded from commits.
For useful error analysis, associate each downloaded output with its producing agent and the original workflow run.
Study-guide topics: post-execution analysis, artifact retrieval, and execution traceability. Reference: GitHub Actions-Downloading workflow artifacts .

NEW QUESTION # 96
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