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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Topic 2: Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Topic 3: Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Topic 4: Design agentic AI solutions | 25% | - Design agent architecture
|
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NEW QUESTION # 34
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner. planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each statement, select Yes or No.
Answer:
Explanation:
NEW QUESTION # 35
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to resolve backlog issues by creating draft pull requests. The repository uses a GitHub Actions workflow for deployments to the production environment.
You discover that the workflow is being triggered before human review.
You need to configure GitHub controls to meet the following requirements:
* The workflow for the agent's draft pull requests must NOT run until a user that has write access approves the pull requests.
* Production deployment jobs must start only after a user with write access explicitly approves the jobs.
Which controls should you configure? To answer, drag the appropriate controls to the correct requirements.
Answer:
Explanation:
Explanation:
Requirement
Control
Agent's draft pull request workflow control
Approve and run the workflows from the pull request merge box.
Production deployment job control
In the deployment job, configure the required reviewers for the production environment.
Approving and running workflows from the pull request interface controls whether workflows associated with the agent's draft pull request are allowed to begin. This provides the required human authorization before CI executes in that context.
Required reviewers on the production environment protect the deployment job itself. When the workflow reaches that environment, GitHub pauses the job until an authorized reviewer explicitly approves it. The approval is therefore applied at the production boundary, independently from the earlier pull-request workflow authorization.
A concurrency group controls overlap but does not require human approval. A custom deployment protection rule may be useful for specialized external validation, but it is not required by the stated scenario. Commit signing and run-summary inspection do not control the requested execution gates.
Study-guide topics: approval gates, protected environments, draft pull-request workflows, and production accountability.
NEW QUESTION # 36
You are evaluating the following workflow YAML.
jobs:
agent1:
runs-on: ubuntu-latest
environment: agent-sandbox
steps:
- uses: actions/checkout@v4
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:
Explanation:
Statement
Answer
agent1 and agent2 jobs will run in parallel.
Yes
If the agent2 job fails, the workflow will fail.
Yes
GitHub Actions jobs run in parallel by default when no needs dependency establishes an execution order.
Therefore, if agent1 and agent2 are independent jobs in the same workflow, they can begin concurrently when runners are available.
A job failure normally causes the workflow to be marked as failed. The workflow can be configured differently through conditional execution, continue-on-error, or dependent job logic, but no such exception is indicated here. An agent2 failure therefore results in a failed workflow outcome under standard behavior.
The environment: agent-sandbox setting identifies an environment associated with agent1. It does not itself create a dependency between agent1 and agent2, nor does it establish workspace isolation between them.
Parallel execution requires separate coordination controls if both jobs can modify shared external resources or deploy to the same target.
Study-guide topics: parallel execution, job dependencies, workflow failure propagation, and execution environments. Reference: GitHub Actions-Workflow syntax .
NEW QUESTION # 37
You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?
Answer: B
NEW QUESTION # 38
Before App1 is upgraded, you need to verify each individual upgrade step and whether all tests have passed.
Which file should you use?
Answer: A
Explanation:
The tasks.md file is the execution-tracking document for the upgrade scenario. It records individual tasks, their validation criteria, and their completion status. Microsoft's upgrade walkthrough specifically directs users to review this file when checking the status of every upgrade step.
A task can include several concrete verification activities, such as restoring dependencies, building the solution, correcting compilation errors, running the test suite, and rerunning tests after fixes. Reviewing these entries reveals whether implementation has merely been attempted or has satisfied the defined checks.
The assessment identifies upgrade issues and scope; it does not serve as the live record of completed work. A plan describes intended actions and ordering, which also differs from evidence that execution and testing succeeded.
The phrase "before App1 is upgraded" requires temporal care. Before execution, tasks.md identifies the steps and checks that must be performed. It cannot prove that future tests have already passed. Completion and test results become available as the agent executes and updates the tasks.
Study-guide topics: execution validation, task status, and acceptance criteria. Reference: Microsoft Learn-Execute and verify a Copilot upgrade.
NEW QUESTION # 39
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