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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access - Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Specify expected outcomes and operational constraints for agent tasks 4. Generate evaluation signals by using automated scanning tools - 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
Topic 2: Implement guardrails and accountability	10–15%	- 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 guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Identify the subset of actions that require human judgment 4. Scope permissions and execution contexts to enforce least-privilege access 5. Block actions that violate defined security, compliance, or Responsible AI policies
Topic 3: Orchestrate multi-agent coordination	15–20%	- Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Configure multi-agent workflows to produce artifacts suitable for review and audit 2. Perform post-hoc analysis of multi-agent behavior 3. Document key decisions, handoffs, and outcomes across agents - 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 - 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. 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
Topic 4: Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement rollbacks 3. Implement escalation paths 4. Implement traceability and accountability for agent actions 5. Implement retries - Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to be invoked in a CI workflow 3. Configure an agent to handle environment-specific constraints 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Evaluate the execution context for an agent 6. Configure an agent to use branch-based scope - Configure MCP servers 1. Configure MCP registries 2. Configure a GitHub remote MCP server 3. Configure MCP allow lists 4. Add an MCP server as a tool to an agent
Topic 5: Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Prevent agent action until the agent checks and approves 3. Configure agent planning to be distinct from agent execution 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 6: 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. 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 - Implement agent memory strategies 1. Choose between short-term, long-term, and external memory 2. Define memory expiration, pruning, and reset rules 3. Scope agent memory to task-relevant information

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Microsoft Developing in Agentic AI Systems Sample Questions (Q82-Q87):

NEW QUESTION # 82
You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?

A. Mention @copilot in a pull request comment to request changes after the plan runs.
B. Add comments to the issues after assigning the issues to the agent.
C. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
D. Add acceptance criteria and directions to the issues before assigning the issues to the agent.

Answer: D

Explanation:
The most effective intervention occurs before the issue becomes an executable assignment. Acceptance criteria define the observable conditions that constitute success, while implementation directions establish boundaries and clarify how the work should proceed. Providing both before assignment reduces the need for the agent to infer missing requirements during planning.
Applied to this scenario, the issue should distinguish required outcomes from optional improvements and identify constraints that the implementation must respect. Where steps are ambiguous, the author should resolve the ambiguity or explicitly require clarification before execution. Reviewable criteria also give the agent and maintainers a shared basis for evaluating whether the resulting changes satisfy the original request.
Options A and D intervene after assignment or execution has begun. They may support later correction, but they do not prevent the initial plan from being based on unclear directions. Option C addresses dependency availability, which is a separate environmental concern.
The governing design principle is to make the task sufficiently definite before delegating action. Relevant curriculum topics include defining inputs and success criteria, validating agent plans, and separating preparation from execution.
Reference:

NEW QUESTION # 83
You have a GitHub Actions workflow that starts multiple agent jobs in parallel.
You need to prevent two runs in the same job from executing simultaneously on the same branch. The solution must ensure, at most, one running instance and one pending instance per branch.
What should you add to the job?

A. jobs.<job_id>.concurrency.cancel-in-progress: ${{ !contains(github.ref, 'release/') }}
B. jobs.<job_id>.strategy.matrix: [1]
C. jobs.<job_id>.strategy.max-parallel: 1
D. jobs.<job_id>.concurrency.group: ci-${{ github.ref }}

Answer: D

Explanation:
A job-level concurrency group coordinates executions that share the same group value across workflow runs. The expression ci-${{ github.ref }} places executions for the same ref into the same group while allowing other refs to use independent groups.
Under the default concurrency behavior, a group permits at most one running execution and one pending execution. When another execution enters an occupied group, it becomes pending; an existing pending execution can be replaced by the newer one. This is a concurrency limit rather than a guarantee that every queued execution will eventually run.
The strategy.max-parallel setting controls simultaneous jobs generated by a matrix within a workflow run. It does not establish the required coordination between separate workflow runs. A matrix definition also does not create a shared concurrency lock. The cancel-in-progress property controls whether an active execution is cancelled, but a cancellation expression alone does not define the necessary group.
Group names should be chosen carefully when multiple jobs or workflows share a repository, because identical names can coordinate those executions too.
Study-guide topics: job synchronization, concurrency groups, and bounded execution. Reference: GitHub Actions-Workflow syntax: job concurrency.

NEW QUESTION # 84
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? 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 # 85
You have a GitHub repository that uses the GitHub Copilot coding agent.
The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue. The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
What should you do?

A. Close and reopen the issue.
B. Create a new issue and assign the issue to Copilot.
C. Post a pull request comment that mentions @copilot.
D. Select Approve and run workflows in the pull request merge box.

Answer: C

Explanation:
A comment mentioning @copilot on the existing pull request provides the direct continuation mechanism. GitHub documents that this can start a new agent session and, by default, push additional commits to the pull request's branch. The comment should explicitly identify the latest repository guidance and the work that remains, correcting the context that produced the outdated proposal.
This approach preserves the existing branch, accumulated changes, review discussion, and relationship to the original issue. It also gives the agent a clear instruction tied to the artifact that must be updated. The commenter must have the required repository write access.
Creating another issue would establish a separate assignment and could duplicate work. Approve and run workflows authorizes GitHub Actions validation; it does not itself resume the coding agent's implementation session. Closing and reopening the issue is likewise not the direct pull-request continuation control.
The useful recovery action therefore combines a new execution trigger with an explicit statement of current intent. Restarting without correcting the outdated requirement could reproduce the same drift.
Relevant curriculum topics are resuming execution, restoring task context, and maintaining continuity without discarding prior work.
Reference:

NEW QUESTION # 86
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. /clear
B. /reset-context
C. /usage
D. /compact

Answer: A

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 # 87
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