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| Section | Weight | Objectives |
|---|---|---|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
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NEW QUESTION # 10
After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?
Answer: B
Explanation:
/compact summarizes the existing conversation history via the model and replaces the raw history with the condensed summary, preserving continuity and prior progress while freeing up context window space -- unlike /clear, which discards everything.
NEW QUESTION # 11
Your team wants Copilot's suggestions to reflect knowledge of internal library APIs that are not publicly documented and not present in the codebase being edited. What is the most appropriate solution?
Answer: B
Explanation:
An MCP server exposing the internal documentation is the appropriate solution because Model Context Protocol extends Copilot with information and capabilities located outside the repository's native context. GitHub describes MCP as a mechanism for integrating Copilot with external systems, enabling it to obtain context or invoke functionality that would otherwise be unavailable from the codebase alone.
This architecture is appropriate for proprietary library documentation because the internal API knowledge can remain centrally maintained rather than being duplicated into every repository. The MCP integration can expose a controlled documentation lookup/search capability, allowing Copilot to retrieve relevant definitions, usage patterns, or internal API information when required.
Option A is inferior because copilot-instructions.md is designed for concise repository-specific instructions and conventions, not as a substitute for a potentially large external documentation corpus. GitHub recommends it for persistent guidance such as building, testing, and repository conventions. Option C would remove context rather than add proprietary knowledge. Plan mode affects workflow sequencing and does not provide new external information.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; MCP-based context augmentation; external knowledge integration; tool-mediated retrieval.
NEW QUESTION # 12
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 # 13
In Microsoft Visual Studio Code, you are using GitHub Copilot Chat to generate documentation for a new feature.
Earlier in the day, you used Copilot Chat extensively for an unrelated refactoring task.
You discover that the Copilot responses for the new documentation task are influenced by the earlier conversation.
You need Copilot to focus only on the current task and avoid using prior conversational context. The solution must NOT affect other conversations.
What should you do?
Answer: D
Explanation:
Starting a new Copilot Chat conversation establishes a separate conversational context for the documentation task. The unrelated refactoring exchanges remain in their original conversation instead of continuing to influence the new thread. This directly addresses the requirement to separate tasks while preserving other conversations.
Conversation history contributes information beyond the latest prompt. Earlier instructions, assumptions, terminology, and design decisions can continue to affect subsequent responses within the same session. A fresh conversation provides a clear task boundary, allowing the developer to supply the feature requirements and relevant documentation context explicitly.
Changing from sidebar chat to inline chat changes the interaction surface; it does not provide the same explicit separation of conversational history. Referencing a file supplies relevant context but does not reliably remove earlier instructions. The maximum requests setting governs agent execution limits rather than conversation isolation. Clearing history is also unnecessary when the previous work can remain available in its own session.
A new conversation can still receive applicable repository instructions and deliberately supplied context; it is the previous conversation that is separated.
Study-guide topics: conversational state, context isolation, and task boundaries. Reference: VS Code-Manage agent sessions.
NEW QUESTION # 14
You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high.
The workflow must proceed automatically only when risk is low.
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:
Target 1 - apply job:
${{ needs.plan.outputs.risk != ' low ' }}
Target 2 - apply_auto job:
${{ needs.plan.outputs.risk == ' low ' }}
The relevant completed workflow is:
apply:
needs: plan
runs-on: ubuntu-latest
environment: approval-required
if: ${{ needs.plan.outputs.risk != ' low ' }}
steps:
- name: Apply agent plan
run: |
echo " Applying plan with risk=${{ needs.plan.outputs.risk }} "
/scripts/apply.sh out/plan.json
apply_auto:
needs: plan
runs-on: ubuntu-latest
if: ${{ needs.plan.outputs.risk == ' low ' }}
steps:
- name: Apply agent plan (auto)
run: |
echo " Auto-applying plan with risk=${{ needs.plan.outputs.risk }} "
/scripts/apply.sh out/plan.json
Comprehensive and Detailed 150 to 250 words of Explanation From Agentic AI Developer/Course Guide/topics:
The apply job must use ${{ needs.plan.outputs.risk != ' low ' }} . Because the only possible values are low, medium, and high, this expression selects both medium- and high-risk plans. The job references the protected environment approval-required , so GitHub Actions can enforce required reviewers before the job is allowed to execute. GitHub environments explicitly support required-reviewer protection rules, and jobs referencing a protected environment remain blocked until the required approval is granted. GitHub Docs The apply_auto job must use ${{ needs.plan.outputs.risk == ' low ' }} . This produces the complementary execution path: only low-risk plans bypass the approval environment and execute automatically.
The plan job exposes risk as a job output, and dependent jobs access that value through the needs. < job > .
outputs. < output > syntax. GitHub documents this mechanism specifically for passing calculated information between jobs. GitHub Docs Using always(), success(), or failure() would evaluate job execution status rather than the risk classification and therefore would not implement the required risk-based guardrail.
Study Guide Reference Topics: Implement Guardrails and Accountability; risk-based execution controls; human-in-the-loop approval; GitHub environments; conditional job execution; job outputs.
NEW QUESTION # 15
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