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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement guardrails and accountability | 10-15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 2: Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
| Topic 3: Implement tool use and environment interaction | 20-25% | - Integrate agents within development environments
|
| Topic 4: Manage memory, state, and execution | 10-15% | - Persist agent state and manage context drift
|
| Topic 5: Perform evaluation, error analysis, and tuning | 15-20% | - Analyze agent failures and identify root causes
|
| Topic 6: Orchestrate multi-agent coordination | 15-20% | - Detect and respond to multi-agent failures and degraded behavior
|
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NEW QUESTION # 18
Drag and Drop Question
You have a GitHub repository that uses GitHub Actions for CI on pull requests.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
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:
Box 1: npm test
npm test (or mvn test, pytest, dotnet test depending on your language stack).
The run keyword requires a shell command to execute your test runner.
Box 2: init
The CodeQL workflow must initialize its database and environment before building code. The official action for this is github/codeql-action/init.
Box 3: analyze
After the code is prepared (and built if using a compiled language), the final step scans the codebase and uploads the SARIF results to GitHub Security. The official action is github/codeql- action/analyze.
Reference:
https://gitprotect.io/blog/github-actions-how-to-use-it-for-security-and-compliance-needs/
NEW QUESTION # 19
You have a GitHub Actions workflow that runs GitHub Copilot-driven integration tests across multiple jobs.
You need to persist the agent memory/state so that it remains available across the jobs.
What should you do?
Answer: C
Explanation:
Workflow artifacts provide an explicit mechanism for transferring serialized agent state between jobs. The producing job writes the required state to files and uploads those files as an artifact. Subsequent jobs download the artifact and load the state before continuing the integration-test workflow.
This is necessary because jobs do not automatically share process memory or a common workspace. They may execute on different runners, and even jobs assigned to similar runner types must not depend on incidental machine reuse. Uploading the state establishes a deliberate persistence boundary rather than relying on temporary execution infrastructure.
Committing state after every job introduces repository mutations merely to transport workflow data. Environment variables are also unsuitable as a general cross-job state store: setting a variable in one job does not automatically expose it in another, and complex state is better represented in structured files.
The application must serialize the information needed for continuation, such as completed steps, relevant decisions, and test progress. Uploading an arbitrary directory does not automatically restore a running agent process.
Relevant curriculum topics are durable state, cross-environment continuity, and resuming work without repeating completed steps.
Reference:
NEW QUESTION # 20
You have a GitHub repository that uses the GitHub Copilot coding agent.
Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
What should you do?
Answer: C
Explanation:
Option D places the token in the secret store intended for the coding agent's execution environment. This gives the agent's tools access to the credential without committing the credential to an agent profile or repository instruction file. The application can consume the injected environment variable when authenticating to the internal scanning API.
GitHub's current interface calls this dedicated category Agents secrets and variables. Its documentation states that secrets previously configured in the repository's copilot environment were automatically migrated to that category. Therefore, D represents the correct agent-specific mechanism using the terminology in the question.
Option A uses the separate Actions secret category, which is not automatically exposed to the cloud agent. Options B and C place sensitive material in repository content, making the token accessible through file access and potentially retained in version history.
Current documentation also confirms that agent secrets are made available as environment variables and their values are masked in session logs. The integration should still avoid deliberately printing credentials or returning them in tool output.
Relevant curriculum topics are secure environment configuration, authenticated tool access, and separation of credential scopes.
Reference:
NEW QUESTION # 21
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: A
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 # 22
You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.
Answer:
Explanation:
NEW QUESTION # 23
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