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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare agent architecture and SDLC processes | 15-20% | - Integrate agents into the software development lifecycle
|
| Topic 2: Implement tool use and environment interaction | 20-25% | - Manage execution environments
|
| Topic 3: Perform evaluation, error analysis, and tuning | 15-20% | - Improve agent behavior
|
| Topic 4: Implement guardrails and accountability | 10-15% | - Ensure accountability
|
| Topic 5: Manage memory, state, and execution | 10-15% | - Manage context and memory
|
| Topic 6: Orchestrate multi-agent coordination | 15-20% | - Coordinate multiple agents
|
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問題 #70
You have a GitHub repository.
You use the GitHub Copilot CLI to run an agentic workflow from the terminal.
During execution, the conversation history approaches the context limit. The agent cannot continue the current session unless the amount of retained context is reduced.
You need to continue the current session without losing all the prior progress.
Which Copilot CLI slash command should you run?
答案：C
解題說明：
You should run the /compact slash command.
Context Management in GitHub Copilot CLI/compact: This command triggers the compaction process manually. It takes a snapshot of your full conversation history, sends it to the AI model to generate a summary, and replaces the bulky history with that concise summary. This reduces token usage instantly while preserving prior progress.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/use-copilot-cli/overview
問題 #71
You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?
答案：C
解題說明：
The correct answer is Edit mode. GitHub Copilot's Edit mode is designed for situations where the developer wants granular control over which files may be changed and what modifications should be applied. In Edit mode, you explicitly select the working set of files, provide natural-language instructions describing the required changes, and then review the proposed edits before accepting or discarding them. GitHub describes Edit mode as appropriate for quick, specific updates to a defined set of files and for scenarios where the developer wants tighter control over the editing process.
Agent mode differs because Copilot determines which files and tools are required, can execute terminal commands, and iterates autonomously toward completing the task. Ask mode is intended primarily for explanations, questions, and code suggestions rather than coordinated file modification. Plan mode generates an implementation strategy before execution and is appropriate when the approach must be reviewed before coding begins.
Therefore, where the requirement explicitly emphasizes selecting specific files and prescribing precise edits rather than delegating scope determination to the agent, Edit mode provides the correct level of developer control.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; selecting appropriate Copilot interaction modes; controlled code modification; human-directed versus autonomous execution.
問題 #72
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?
答案：A
問題 #73
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
答案：A,E
解題說明：
The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
CodeQL findings that identify security vulnerabilities in the generated code.
The detection of hardcoded secrets, such as API keys and tokens.
Reference:
https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/
問題 #74
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?
答案：C
解題說明：
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:
問題 #75
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