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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Design agentic AI solutions | 25% | - Design agent architecture
|
>> Microsoft GH-600 Exam Outline <<
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NEW QUESTION # 44
You are analyzing the following agent logs.
You need to classify the error in a report for your company's CTO.
How should you classify the error?
Answer: C
Explanation:
This is a Tool misuse error (specifically, a configuration/implementation bug).The agent successfully passed the reasoning and context phases, but the environment's internal handler failed because the mcp_microsoftdocs_microsoftdocs_search tool was not properly registered or the IDE failed to process the tool call's schema.
Reference:
https://github.com/anthropics/claude-quickstarts/issues/345
NEW QUESTION # 45
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:
NEW QUESTION # 46
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?
Answer: A
Explanation:
Adding a clear description of the problem to the issue will directly remedy this problem.Coding agents like GitHub Copilot rely heavily on the context, details, and constraints provided in the issue to understand what needs to be fixed. A one-line description like "Fix the login bug" is too vague, leading to guesswork, incorrect scope, and incomplete code changes.
Defines Scope: Explicitly stating what is broken prevents the agent from changing unrelated files.
Identifies the Root Cause: Providing error logs or steps to reproduce guides the agent to the exact lines of code that need fixing.
Sets Expectations: Specifying the expected correct behavior ensures the agent generates all the necessary changes, preventing missing code.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 47
You are running the GitHub Copilot CLI and want the agent to execute file edits and shell commands without prompting for confirmation on each individual action, during a trusted throwaway sandbox session. Which flag/command should you use?
Answer: D
Explanation:
The --allow-all (commonly nicknamed "YOLO mode") flag disables per-action confirmation prompts, letting the agent run autonomously. This should only be used in isolated, low-risk environments since it removes a key safety checkpoint.
NEW QUESTION # 48
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues in an ephemeral GitHub Actions environment.
Builds fail unless an environment variable named NPM_TOKEN is available, because the repository depends on a private package registry.
A workflow named .github/workflows/copilot-setup-steps.yml was added but was merged into a non-default branch and does NOT run when new agent sessions start.
You need to ensure that for every new agent session, the setup steps run, and the private registry token is available as NPM_TOKEN, without hardcoding secrets in the repository.
What should you do?
Answer: C
Explanation:
The Copilot setup workflow must be present on the repository's default branch to be used for new coding-agent sessions. Moving .github/workflows/copilot-setup-steps.yml there ensures the setup procedure is discoverable for every newly created agent environment.
The package-registry credential should be stored as an environment secret in the Copilot environment and exposed using the required variable name, NPM_TOKEN. This permits package installation without committing the token into workflow YAML, instructions, source code, or pull requests.
Disabling network controls does not resolve missing credentials and expands risk unnecessarily. Repository instructions can describe setup expectations but cannot safely substitute for an injected runtime secret. Adding the raw token to YAML permanently exposes it to repository history and anyone with repository access.
Study-guide topics: ephemeral environments, setup workflows, environment secrets, and secure dependency access.
NEW QUESTION # 49
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