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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Specify expected outcomes and operational constraints for agent tasks 3. Align evaluation criteria with development intent 4. Generate evaluation signals by using automated scanning tools - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints - 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
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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Prevent conflicting context 3. Share agent state - 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 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. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Scope permissions and execution contexts to enforce least-privilege access 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Identify the subset of actions that require human judgment
Orchestrate multi-agent coordination	15–20%	- Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Identify failed, partial, or stalled agent executions 3. Respond to degraded behavior or coordination across agents - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Perform post-hoc analysis of multi-agent behavior 2. Document key decisions, handoffs, and outcomes across agents 3. Configure multi-agent workflows to produce artifacts suitable for review and audit - 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 the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows
Implement tool use and environment interaction	20–25%	- Select and configure agent tools 1. Configure agent tool permissions 2. Identify required tools 3. Configure agent tools - Integrate agents within development environments 1. Configure an agent to handle environment-specific constraints 2. Configure an agent to be invoked in a CI workflow 3. Configure an agent's scope to a specific repository 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 - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP registries 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 rollbacks 3. Implement escalation paths 4. Implement traceability and accountability for agent actions 5. Implement error handling
Prepare agent architecture and SDLC processes	15–20%	- Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Define inputs, outputs, and success criteria for agents 3. Identify and mitigate common anti-patterns in agents - Configure observability and control for autonomous agents 1. Configure human intervention for autonomous agents without slowing delivery 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Plan and implement the degree of agent autonomy, including guardrails - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure an agent to output a structured plan 3. Configure agent planning to be distinct from agent execution 4. Prevent agent action until the agent checks and approves

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Microsoft Developing in Agentic AI Systems Sample Questions (Q93-Q98):

NEW QUESTION # 93
You have a GitHub Actions workflow that runs two jobs named agent-plan and agent-implement on pull requests.
The agent-plan job generates an implementation plan that downstream jobs must consume, but the job currently writes the plan only to the console.
You need to capture the implementation plan as a step output and configure the workflow so that downstream jobs can reference the output.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Correct selection
Agent output capture method
Write plan=... to $GITHUB_OUTPUT.
Downstream reference expression
${{ needs.agent-plan.outputs.plan }}
GitHub Actions distinguishes console logs, environment variables, step outputs, and job outputs. Writing the implementation plan to the console makes it visible in logs but does not create an output that another job can reference. The producing step must write a named value to the file identified by GITHUB_OUTPUT.
The workflow must also expose that step output as a job output. For example, a step with the identifier generate can publish plan, and the agent-plan job can map its outputs.plan property to ${{ steps.generate.
outputs.plan }}. The consuming job declares needs: agent-plan and reads ${{ needs.agent-plan.outputs.plan
}}. Both the output mapping and the dependency declaration are necessary for the intended cross-job contract.
The screenshot's displayed GITHUB_ENV selection is incorrect. That mechanism makes environment variables available to subsequent steps in the same job; it does not publish a downstream job output. A multiline plan additionally requires the supported multiline output syntax.
The relevant curriculum topics are sharing agent state, maintaining execution continuity, and preserving decisions across workflow stages.
Reference: Passing information between jobs .

NEW QUESTION # 94
You have a GitHub Enterprise repository that uses the GitHub Copilot coding agent and opens draft pull requests for assigned issues.
Evaluation results show that the agent repeatedly opens pull requests that modify .github/workflows/*.yml to bypass failing checks instead of fixing the underlying code.
You need to ensure that the agent fixes the underlying code instead of bypassing the failing checks.
What should you do?

A. Start the GitHub Copilot CLI by using --allow-all.
B. Disable GitHub Actions workflows for pull requests created by the agent.
C. Update the .gitignore file.
D. Create a branch ruleset.

Answer: D

Explanation:
A properly configured branch ruleset establishes enforcement outside the agent's editable task instructions. It can require pull requests, successful checks, and appropriate reviews before changes enter protected branches.
In an enterprise setup, required workflows can also be governed centrally so that changing a repository-local workflow does not remove the organization's validation requirement.
The ruleset must be configured to preserve the intended checks. Merely creating an empty ruleset does not solve the problem, and requiring a check whose implementation the agent can freely weaken is insufficient.
Protecting workflow changes through required ownership review or centrally managed validation addresses that remaining gap. The agent should not receive bypass permission for these controls.
Option A increases authority rather than constraining the unwanted behavior. Option C removes the checks that are supposed to detect defective code. Option D affects treatment of untracked files and does not prevent changes to already tracked workflow definitions.
The control cannot guarantee successful reasoning, but it can prevent acceptance of a shortcut that defeats validation.
Relevant curriculum topics are blocking prohibited actions, enforcing independent controls, and correcting undesirable behavior identified through evaluation.
References: Available rules for rulesets , Enterprise required workflows .

NEW QUESTION # 95
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.group: ci-${{ github.ref }}
B. jobs. < job_id > .strategy.max-parallel: 1
C. jobs. < job_id > .strategy.matrix: [1]
D. jobs. < job_id > .concurrency.cancel-in-progress: ${{ !contains(github.ref, ' release/ ' ) }}

Answer: A

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 # 96
You have a GitHub Copilot Enterprise subscription.
Developers use Microsoft Visual Studio Code.
You need to configure an array of subagents that can be invoked by an agent.
What should you configure in the YAML frontmatter for the agent?

A. target
B. tools
C. agents
D. mcp-servers
E. handoffs

Answer: C

Explanation:
The agents frontmatter property defines the custom agents that the current agent can invoke as subagents. It establishes the delegation boundary for an orchestrator or other coordinating agent.
The tools property grants operational capabilities such as file access, terminal commands, MCP tools, or the agent invocation tool itself. It does not identify the specific custom-agent profiles available for delegation. The mcp-servers property configures external MCP servers; it does not define subagent membership.
Handoffs are user-facing transitions from one custom agent to another. They are useful for moving a conversation from planning to implementation but are not the configuration mechanism for an agent's available subagent array. The target field identifies an execution target or context and is unrelated to delegation membership.
For a reliable orchestration design, include only the required specialized agents in the agents list and ensure the parent agent has the appropriate agent-invocation capability.
Study-guide topics: subagent lists, delegation boundaries, orchestrator design, and multi-agent coordination.

NEW QUESTION # 97
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository has a ruleset named ruleset1 that enforces the following:
* Signed commits
* Branch protections that require status checks to pass before merge
The agent is blocked from operating in the repository because it fails to comply with the signed-commits rule.
You need to ensure that the agent can create and push changes to copilot/ branches. The solution must enforce the signed-commits rule for human developers on protected branches.
What should you do?

A. Make Copilot an owner of the repository.
B. Grant the Copilot coding agent permission to push directly to the copilot/ branches.
C. Add Copilot as a bypass actor for ruleset1.
D. Remove the Require signed commits rule from ruleset1.

Answer: C

Explanation:
Adding Copilot as a bypass actor for the ruleset permits the coding agent to create and push its working changes without removing the signed-commit control for human developers. This is the narrowly scoped exception required by the scenario.
Making Copilot a repository owner grants excessive privilege and is not needed to resolve the signing restriction. Granting general push permission does not override a ruleset that blocks unsigned commits.
Removing the signed-commit requirement weakens protection for every actor governed by the ruleset, including human developers on protected branches.
A bypass actor should be used deliberately and limited to the required automated identity and scope. The protected branch requirements, status checks, and human review processes should remain in place before agent-generated changes are merged.
Study-guide topics: rulesets, bypass actors, branch protection, and least-privilege exceptions.

NEW QUESTION # 98
......

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