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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Scope permissions and execution contexts to enforce least-privilege access - 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
Topic 2: Manage memory, state, and execution	10-15%	- 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 - Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information
Topic 3: 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 - 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 - 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
Topic 4: Implement tool use and environment interaction	20-25%	- Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP allow lists 4. Configure the MCP registries - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement escalation paths 3. Implement rollbacks 4. Implement retries 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 - 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. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Evaluate the execution context for an agent 5. Configure an agent to handle environment-specific constraints 6. Configure an agent to use branch-based scope
Topic 5: Prepare agent architecture and SDLC processes	15-20%	- 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 - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform - Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checked and approved 2. Validate agent plans 3. Configure agent planning to be distinct from agent execution 4. Configure an agent to output a structured plan
Topic 6: 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 - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access - 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. Specify expected outcomes and operational constraints for agent tasks 4. Align evaluation criteria with development intent

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Microsoft Developing in Agentic AI Systems Sample Questions (Q107-Q112):

NEW QUESTION # 107
You have a GitHub repository that uses GitHub Copilot Chat in Microsoft Visual Studio Code. Custom agents are stored in the repository under version control.
Your team uses a multi-agent workflow where a planner agent produces an implementation plan that is then handed off to an implementation agent to make changes.
Recent prompts cause the planner agent to start editing files and running commands before the plan is approved.
You need to configure the planner agent to meet the following requirements:
Use only read-only tools.
Hand off to the implementation agent only after the plan is approved.
How should you configure the agent? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 108
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. Grant the Copilot coding agent permission to push directly to the copilot/ branches.
B. Make Copilot an owner of the repository.
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 # 109

Answer:

Explanation:

NEW QUESTION # 110
You have a repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. Risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high.
The workflow must proceed automatically only when risk is low.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

Explanation:
Target
Value
approval_required.if
${{ needs.plan.outputs.risk != ' low ' }}
apply_auto.if
${{ needs.plan.outputs.risk == ' low ' }}
The approval job must run whenever the computed risk is not low. The expression ${{ needs.plan.outputs.risk
!= ' low ' }} includes both medium and high risk values, routing those plans through the protected approval environment.
The automatic application job must run only when the plan output explicitly equals low. The expression ${{ needs.plan.outputs.risk == ' low ' }} prevents the automatic path from running for medium, high, missing, or unexpected risk values.
These conditions should be evaluated from a defined output of the preceding plan job. The plan job must therefore parse plan.json, validate the risk value, and expose it through jobs. < job_id > .outputs. A malformed, empty, or unknown risk value should not fall through to automatic execution.
The environment gate supplies the human confirmation mechanism. The conditional logic only routes jobs; it does not itself create approval requirements.
Study-guide topics: risk-based routing, conditional workflow execution, protected environments, and agent autonomy controls.

NEW QUESTION # 111
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
You have the following Model Context Protocol (MCP) configuration in Visual Studio Code:
{
" servers " : {
" mcp1 " : {
" command " : " npx " ,
" args " : [ " -y " , " @microsoft/mcp-server-test " ]
},
" mcp2 " : {
" command " : " uvx " ,
" args " : [ " mcp-server-test " , " --db-path " , " pubs.db " ]
}
}
}
For each statement, select Yes if the statement is true. Otherwise, select No.

Answer:

Explanation:

Explanation:
Statement
Answer
mcp1 is a Python-based MCP server.
No
mcp2 is a Python-based MCP server.
Yes
mcp1 and mcp2 are configured to use a specific package version.
No
The npx command runs packages through the Node.js ecosystem, so mcp1 is not configured as a Python- based MCP server. Its package name is supplied without an explicit version suffix.
The uvx command runs Python tools and packages through the uv ecosystem. Therefore, mcp2 is configured as a Python-based MCP server. Its --db-path argument supplies server-specific configuration; it does not pin a package release.
Neither server specifies a version, such as @package@1.2.3 for an npm package or an equivalent version constraint for a Python package. Without a pinned version, the resolved package may change over time, which can affect reproducibility and behavior.
Study-guide topics: MCP server configuration, Node.js tooling, Python tooling, and dependency version control.

NEW QUESTION # 112
......

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