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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
|
| Design agentic AI solutions | 25% | - Design agent architecture
|
| Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
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NEW QUESTION # 77
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 # 78
Drag and Drop Question
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent.
You need to ensure that the default GITHUB_TOKEN permissions are read-only, and write access is granted to only the job that performs repository write operations. The workflow must be able to create and approve pull requests only when explicitly enabled.
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:
Explanation:
How should you complete the workflow?
Box 1: contents: read
Set Global Read-Only Permissions
To enforce the principle of least privilege, the top-level permissions block must strip all default write capabilities from the GITHUB_TOKEN. Setting contents: read allows the workflow to fetch the code but prevents any accidental repository modifications.
Box 2: contents: write
Grant Job-Level Write Access
Only the specific job responsible for modifying the repository should receive write access. The update_artifacts job requires permission to push code modifications back to the repository.
Box 3: pull-requests: write
To successfully create and approve pull requests, you must explicitly enable this capability in your GitHub repository settings:
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/disabling-or-limiting-github-actions-for-your-organization
NEW QUESTION # 79
You need to resolve the scoping issue associated to agent1.
What should you do?
Answer: B
Explanation:
A fine-grained PAT restricted to product-api provides an enforceable repository-level authorization boundary for agent1. GitHub fine-grained personal access tokens can be limited to selected repositories and assigned only the repository permissions required by the workload. Consequently, even if agent1 attempts to access billing-service or infra-terraform, the credential does not authorize access to those repositories. GitHub specifically recommends fine-grained PATs when repository access must be restricted to particular repositories.
This directly aligns with the GH-600 objective to configure an agent's scope to a specific repository and define execution boundaries through repository context and permissions. Microsoft Learn defines execution context as including the repository an agent can access and emphasizes that repository scope is the first boundary limiting agent behavior.
Option A provides behavioral guidance but is not a security authorization boundary. Option B controls the permissions available to the workflow token but does not, by itself, establish the required cross-repository credential restriction. Option D only prevents certain push operations; it does not stop agent1 from reading or otherwise accessing the other repositories.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction โ execution context and boundaries; repository-specific agent scope; tool permissions; least-privilege authentication.
NEW QUESTION # 80
You have a GitHub repository that uses three GitHub Copilot coding agents named agent1, agent2, and agent3.
During structured evaluation runs, agent1 frequently returns Markdown narratives instead of a machine- parsable result.
You need to ensure that agent1 consistently returns only a predefined JSON structure without affecting the output of the other agents.
Which file should you modify?
Answer: C
Explanation:
The required change concerns one agent's behavior, making its custom agent profile the appropriate scope.
The file .github/agents/agent1.agent.md defines instructions for agent1. Its Markdown instruction body can specify the required JSON structure, mandatory properties, allowed values, and prohibition of explanatory prose or Markdown fences.
Repository-wide instructions apply more broadly and could alter the behavior of agent2 and agent3, contrary to the requirement. The setup workflow prepares the execution environment rather than defining the agent's response contract. A file under .github/instructions uses instruction applicability rules; naming it after an agent does not automatically bind it exclusively to that agent.
The evaluation finding should be converted into a precise behavioral requirement and tested against representative inputs. For example, checks should distinguish valid JSON from text containing a JSON fragment and should reject missing fields or unexpected properties.
An agent instruction improves adherence but is not a mathematical guarantee of schema compliance.
Production consumers should validate the returned object before using it. Relevant curriculum topics are revising instructions based on evaluation results and specifying expected output constraints.
Reference: Custom agents configuration .
NEW QUESTION # 81
An enterprise administrator wants to audit which repositories have had Copilot coding agent- created pull requests over the last 30 days. Where should the administrator look?
Answer: A
Explanation:
GitHub's organization/enterprise audit log records agent activity, including pull requests created by the Copilot coding agent, giving administrators visibility for compliance and governance purposes.
NEW QUESTION # 82
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