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| Section | Weight | Objectives |
|---|---|---|
| Implement tool use and environment interaction | 20-25% | - Select and configure agent tools
|
| Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
| Manage memory, state, and execution | 10-15% | - Implement agent memory strategies
|
| Implement guardrails and accountability | 10-15% | - Define autonomy levels
|
| Perform evaluation, error analysis, and tuning | 15-20% | - Tune agent behavior based on evaluation results
|
| Orchestrate multi-agent coordination | 15-20% | - Operate and manage multi-agent workflows
|
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NEW QUESTION # 48
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:
Explanation:
Target
Value
Save logs action
actions/upload-artifact@v4
Artifact name
agent-session-${{ github.run_id }}-${{ github.sha }}
Artifact path
agent/session/
actions/upload-artifact@v4 captures the generated session-log directory as a workflow artifact. The correct path is .agent/session/, which contains the log file produced during the agent execution.
Using both github.run_id and github.sha in the artifact name links the artifact to a unique workflow execution and the exact source revision associated with that execution. This makes later audit and troubleshooting more reliable than a generic artifact name that may be repeated across runs.
actions/download-artifact retrieves an existing artifact and cannot upload current-session evidence. Storing the logs under .github/workflows/ would incorrectly identify the workflow-definition directory rather than the session-output directory.
Artifact retention should be configured according to the organization's audit requirements. The workflow should also avoid writing secrets into session logs before uploading them.
Study-guide topics: execution artifacts, traceability, workflow auditing, and durable session records.
NEW QUESTION # 49
You need to make changes to repo1 to support the planned changes for the developers.
What should you modify?
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 # 50
You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
Explanation:
Target 1 # E. needs
Target 2 # A. [spec_analyzer, risk_reviewer]
Target 3 # B. concurrency
The relevant completed section is:
plan_merger:
runs-on: ubuntu-latest
needs: [spec_analyzer, risk_reviewer]
steps:
- name: Merge executor outputs into a single plan
run: ./executors/plan_merger.sh
- name: Publish merged plan
run: echo " publish plan artifact "
concurrency:
group: multiagent-${{ github.ref }}
The plan_merger job must use needs: [spec_analyzer, risk_reviewer] . GitHub Actions jobs run in parallel by default when no dependency exists. Therefore, spec_analyzer and risk_reviewer can execute concurrently, implementing the fan-out phase. The needs array then establishes the fan-in dependency: plan_merger does not start until both upstream jobs complete successfully. GitHub explicitly defines jobs. < job_id > .needs as the mechanism for declaring jobs that must complete before another job runs.
The final blank is concurrency , because the following line already defines group: multiagent-${{ github.ref
}}. GitHub ' s concurrency key groups workflow jobs or runs under a shared key so that overlapping executions for the same reference can be controlled. This is separate from the parallelism of the two specialized analyzer jobs.
This architecture directly implements a multi-agent orchestration pattern: independent specialized executors operate concurrently, their outputs converge at a deterministic coordination point, and execution boundaries are managed explicitly. The GH-600 study guide specifically requires candidates to apply orchestration patterns, configure parallel agent execution, and detect or prevent conflicts among concurrently operating agents.
Study Guide Reference Topics: Orchestrate Multi-Agent Coordination; fan-out/fan-in orchestration; parallel execution; dependency management; agent isolation and conflict control.
NEW QUESTION # 51
You have a GitHub repository that uses the following GitHub Copilot CLI command in a Bash script.
#!/usr/bin/env bash
set -euo pipefail
PROMPT="Update dependencies, run tests, and open a pull request if changes are needed." copilot --autopilot --yolo --max-autopilot-continues 10 -p "$PROMPT" For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 52
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?
Answer: E
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 # 53
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