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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
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NEW QUESTION # 76
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 # 77
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 78
A designated top-level instructions file is used by some agentic tools to describe overall repository purpose, build/test commands, and conventions in a tool-agnostic way (usable across multiple AI coding agents, not just Copilot). What is this file commonly called?
Answer: A
Explanation:
agents.md (or AGENTS.md) is an emerging convention for providing agent-agnostic project context - build steps, structure, conventions -- that multiple AI coding tools (not just Copilot) can consume.
NEW QUESTION # 79
You have a GitHub repository that uses the GitHub Copilot CLI to run autonomous tasks.
You need to validate each generated command before it runs. Any commands that attempt to modify paths outside the repository must be blocked.
How should you complete the YAML? 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 # 80
You are debugging an agentic workflow that intermittently fails specific tool calls with rate-limit errors when connecting to an internal MCP server. What is the most direct remediation?
Answer: D
Explanation:
Rate-limit errors from a tool call indicate the MCP server's quota configuration is being exceeded; adjusting the server's rate limits addresses the root cause, unlike context-management or permission commands.
NEW QUESTION # 81
......
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