Free PDF Quiz The Best GH-600 - New Soft Developing in Agentic AI Systems Simulations

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Perform evaluation, error analysis, and tuning	15-20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Generate evaluation signals by using automated scanning tools 4. Align evaluation criteria with development intent - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage - 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
Topic 2: Manage memory, state, and execution	10-15%	- 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 - Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Detect and correct drift during extended agent execution 3. Resume agent work without repeating steps or diverging from prior decisions - Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent stale context 3. Prevent conflicting context
Topic 3: Orchestrate multi-agent coordination	15-20%	- 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. 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. Configure agent isolation for parallel execution 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Respond to degraded behavior or coordination across agents 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop
Topic 4: Prepare agent architecture and SDLC processes	15-20%	- Integrate agents into the software development lifecycle (SDLC) 1. Define inputs, outputs, and success criteria for agents 2. Identify steps for agents to perform 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. Plan and implement the degree of agent autonomy, including guardrails 3. Configure agents to produce inspectable artifacts within standard development tooling - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure agent planning to be distinct from agent execution 3. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan
Topic 5: 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. 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
Topic 6: Implement tool use and environment interaction	20-25%	- Configure MCP servers 1. Configure MCP allow lists 2. Configure the MCP registries 3. Configure a GitHub remote MCP server 4. Add an MCP server as a tool to an agent - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Configure an agent's scope to a specific repository 3. Configure an agent to use branch-based scope 4. Configure an agent to handle environment-specific constraints 5. Evaluate the execution context for an agent 6. Configure an agent to be invoked in a CI workflow - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement rollbacks 4. Implement escalation paths 5. Implement error handling

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Microsoft Developing in Agentic AI Systems Sample Questions (Q24-Q29):

NEW QUESTION # 24
You have a GitHub repository that contains the following custom agent files:
A file named planner.agent.md that includes YAML frontmatter with a handoffs entry that has label: Start Implementation, agent: implementer, and prompt: Now implement the plan outlined above.
A file named implementer.agent.md that is in the same directory as planner.agent.md and includes YAML frontmatter that has name: IMPLEMENTER.
You add a third agent file named review.agent.md that includes name: code-review in the YAML frontmatter.
You make the following changes to planner.agent.md:
Update the existing handoff to include send: true and model: GPT-5.2 (copilot).
Add a second handoff that has label: Run Review, agent: code-review, and prompt: Review the code changes made in the previous step.

Answer:

Explanation:

NEW QUESTION # 25
You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 26
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent that deploys a containerized service.
You need to update the workflow so that only one production deployment can run at a time, and any currently running production deployments are cancelled when a new run starts.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 27
You need to make changes to repo1 to support the planned changes for the developers.
What should you modify?

A. .vscode/settings.json
B. <project>/.mcp/server.json
C. .github/agents/*.agent.md
D. .vscode/mcp.json

Answer: C

Explanation:
The selected location is appropriate when the planned developer changes concern repository-defined custom agents. Files under .github/agents/ define reusable agent roles that can be stored and reviewed alongside the application code. Their .agent.md format combines YAML configuration with Markdown instructions describing the agent's responsibilities and expected behavior.
This arrangement allows the team to maintain specialized planning, implementation, and review roles consistently. Changes to an agent's instructions or configuration can pass through the repository's normal pull request process, providing a record of how the development workflow evolves.
The alternative locations serve different configuration purposes. .vscode/mcp.json configures MCP server connections for the workspace. .vscode/settings.json contains editor settings. Neither is the primary location for defining a repository's custom agent profile.
The question's planned changes are not reproduced in its supplied statement. Therefore, B is the source-selected answer under the custom-agent interpretation; a requirement specifically about MCP connectivity would require different configuration.
Study-guide topics: custom agent architecture, repository-scoped configuration, and version-controlled development workflows. Reference: VS Code-Custom agent file locations and structure.
Topic 2, GitHub Environment
Existing Environment
GitHub Environment
The GitHub environment contains the following:
Three repositories named product-api, billing-service, and infra-terraform.
Branch protection on the main branch in all repositories that requires at least one pull request review before merging GitHub Actions runners used across all workflows A GitHub team named SG_Dev that contains developers A GitHub team named SG_Review that contains senior engineers and a security team A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
No custom agent profile is defined.
A Model Context Protocol (MCP) server named MCP1 is deployed to https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs. MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope. agent1 makes code changes immediately after receiving a task.
A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes. Other developers report this intermittently as well.
Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs. agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
Ensure that agent1 can access all the tools in the environment.
Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
Ensure that Copilot retains details that it has learned and uses that knowledge for future work. This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
Agent workflows must be able to run in parallel.
Application error handling must use the repository ErrorHandler class. agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security Requirements
Litware identifies the following security requirements:
Only the members of SG_Review must be able to approve agent1 plan outputs.
All API keys must be stored and accessed securely.
The developers must NOT be able to self-approve.
Agent configuration

NEW QUESTION # 28
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. Disable GitHub Actions workflows for pull requests created by the agent.
B. Create a branch ruleset.
C. Update .github/copilot-instructions.md.
D. Start the GitHub Copilot CLI by using --allow-all.

Answer: B

Explanation:
To prevent the GitHub Copilot coding agent from modifying your workflow files, you should configure GitHub branch protection rules or repository rulesets to restrict modifications to the .github/workflows/ directory.
You should create a branch ruleset.
Creating a branch ruleset with a "Restrict file paths" rule is the most effective solution. By blocking any pull request from modifying .github/workflows/*, you force the agent to find solutions within the codebase rather than altering the CI/CD pipeline.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 29
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