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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Topic 2: Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Topic 3: Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Topic 4: Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
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NEW QUESTION # 12
In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
You need to ensure that the script meets the following business requirements:
Planning must be kept separate from executing.
Each plan must be captured without calling any tools.
Each webhook event must create a new independent session.
Once the plan is complete, tool calls must be enabled within the same event-specific session for follow-up.
Answer:
Explanation:
NEW QUESTION # 13
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?
Answer: B
Explanation:
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents
NEW QUESTION # 14
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 # 15
You have a GitHub repository that uses the GitHub Copilot coding agent. The repository uses strict branch protections on the main branch.
You maintain a long-lived branch named release/1.4 that has different dependencies and CI checks.
You need the agent to create a hotfix that is implemented and validated against the release/1.4 branch. The solution must prevent the agent from basing its work on main.
What should you do?
Answer: D
Explanation:
Selecting release/1.4 as the base branch establishes the correct repository context before the agent begins its assignment. GitHub documents that Copilot creates its working branch from the selected base branch. This ensures that the initial code, dependency declarations, and branch-specific files correspond to the release being repaired.
That distinction matters because a hotfix developed against main may depend on APIs, package versions, or build behavior that do not exist in the maintained release. Choosing the release branch at task creation removes the need for the agent to infer or subsequently correct its starting point. The task should also require validation using the release's applicable checks.
Option A controls concurrency rather than branch selection. Option B governs review requirements for changes to main; it does not determine where the agent starts. Option C supplies a natural-language instruction but is less direct than configuring the actual base branch before execution.
The selected answer configures the execution environment through the platform's supported task controls.
Relevant curriculum topics are branch-based scope, execution-context selection, and environment-specific constraints.
Reference:
NEW QUESTION # 16
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You live stream the session log output and see the following.
* agent execution (ID 987654321)
Run ./scripts/agent-run.sh
--------------------------------
[agent] Starting task: Clean up infra configs
[agent] Executing step: Remove unused files
rm -rf /infra
Error: Command blocked by policy
Reason: destructive_operation_detected
[agent] Escalating to human review...
X Run agent task
X Process completed with exit code 1
X agent-execution failed
What is a possible cause of the error?
Answer: D
Explanation:
A preToolUse hook is a possible cause because it evaluates a proposed tool invocation before execution. Such a hook can inspect the tool name and arguments, identify a prohibited operation, and deny permission. A shell command containing recursive, forced deletion of an infrastructure directory is an appropriate target for this type of preventive rule.
The decisive evidence is the statement that the command was blocked because a destructive operation was detected. This describes a decision about the proposed command rather than a failure to reach a network destination. Copilot firewall controls address network access; they do not ordinarily classify local filesystem deletion commands. A postToolUse hook runs after a tool operation and therefore does not provide the same pre-execution interception.
An application installation policy governs application access or installation rather than this command-level decision. The surrounding script can treat a denied operation as a task failure and escalate it for human review, producing the displayed exit status.
The answer identifies a plausible enforcement mechanism; the log does not establish its exact implementation.
Study-guide topics: preventive guardrails, tool authorization, destructive-action detection, and escalation. Reference: GitHub Copilot-Hooks reference.
NEW QUESTION # 17
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