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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Orchestrate multi-agent coordination | 15-20% | - Detect and respond to multi-agent failures and degraded behavior
|
| Topic 2: Implement tool use and environment interaction | 20-25% | - Operate agents with safe execution paths and robust error handling
|
| Topic 3: Perform evaluation, error analysis, and tuning | 15-20% | - Tune agent behavior based on evaluation results
|
| Topic 4: Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
| Topic 5: Implement guardrails and accountability | 10-15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 6: Manage memory, state, and execution | 10-15% | - Persist agent state and manage context drift
|
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NEW QUESTION # 79
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:
Explanation:
Statement
Answer
The Run Review handoff resolves to review.agent.md.
Yes
The Start Implementation handoff fails to identify the target agent.
No
Selecting Start Implementation switches the chat to the implementer agent and automatically submits the handoff prompt to that agent by using GPT-5.2 (copilot).
Yes
A handoff identifies the destination agent separately from the label displayed to the user. The Run Review entry targets code-review, which is the declared name of the agent defined in review.agent.md. The filename therefore does not need to match the handoff's visible button label or the declared agent name.
The Start Implementation entry identifies the implementation agent. The uppercase display name IMPLEMENTER does not, in this scenario, make the existing implementation handoff invalid. Its purpose remains to transfer the planning conversation into the implementation role.
The send property determines what happens to the handoff prompt after the transition. With send: true, selecting the handoff submits the prompt automatically instead of merely placing it in the input box for editing. The model property selects the model for that handoff using the qualified model-and-provider format.
Successful use assumes that the specified model is available to the user.
These properties control destination, prompt submission, and model selection independently; the button label is only the user-facing action name.
Study-guide topics: agent routing, handoff configuration, and execution transitions. Reference: VS Code- Custom agent handoffs and frontmatter .
NEW QUESTION # 80
You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
How should you complete the workflow? 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 # 81
During an agentic session, you want to see exactly how many tokens are being consumed by the system prompt, tools, and message history before deciding whether to compact. Which command should you run?
Answer: A
Explanation:
/context displays a breakdown of the active context window, including tokens used by the system prompt, MCP/system tools, and message history, along with remaining free space.
NEW QUESTION # 82
Case Study 2
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
You need to configure agent1 to support the planned changes.
What should you do?
Answer: E
Explanation:
Scenario: 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.
The correct action to take is to delete the line with tools: ['read','cearch','edit'] from the agent configuration.
Enabling All Tools: In GitHub Copilot Agent configuration specifications, omitting the tools key entirely or deleting it allows the agent to automatically inherit and utilize all available tools in the runtime environment. Explicitly hardcoding a restricted array limits its capabilities.
Targeted Instructions: Modifying the repository's configuration for the agent ensures that the specific product-api guidelines apply strictly to that custom agent without bleeding into general Copilot Chat or standard Copilot code reviews.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-sdk/features/mcp
NEW QUESTION # 83
You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?
Answer: D
Explanation:
Repository memory is supported by citations to the code that establishes the remembered fact. When a fact appears relevant to a new task, Copilot checks its supporting citations against the current branch before relying on it. This validation prevents an observation from an earlier repository state from being treated automatically as current truth.
The branch check is particularly important when work spans release branches, experimental changes, or pull requests that were never merged. A memory may have been accurate when created but no longer describe the code now being edited. If its supporting code is absent or no longer supports the claim, the remembered fact should not influence the task.
Option A incorrectly describes memory as permanently retained information requiring only manual intervention. Option B substitutes pull-request disposition for validation of the underlying evidence. Option C incorrectly treats repository knowledge as usable only by its original contributor.
The governing mechanism is evidence validation at the point of use, not an assumption that all previously stored information remains authoritative.
Relevant curriculum topics are task-relevant memory, stale-context prevention, and maintaining state consistency across branches and sessions.
Reference:
NEW QUESTION # 84
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