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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement agents and multi-agent systems30%- Build agents with Azure AI tools and frameworks
  • 1. Develop using Semantic Kernel and Azure AI Foundry
    • 2. Implement agent logic and reasoning
      • 3. Integrate models and prompts
        - Orchestrate multi-agent collaboration
        • 1. Manage agent handoffs and task distribution
          • 2. Implement workflows and coordination strategies
            • 3. Define communication protocols between agents
              Topic 2: Integrate tools, data, and services25%- Connect data sources and knowledge bases
              • 1. Implement retrieval-augmented generation (RAG)
                • 2. Integrate vector databases and search
                  • 3. Ensure data security and access control
                    - Incorporate external tools and APIs
                    • 1. Handle authentication and error resilience
                      • 2. Design and register tool definitions
                        • 3. Implement function calling and service integration
                          Topic 3: Test, deploy, and monitor agentic AI systems20%- Validate agent performance and safety
                          • 1. Apply guardrails and content safety
                            • 2. Evaluate quality metrics and iterate
                              • 3. Test reasoning accuracy and consistency
                                - Deploy and monitor agents at scale
                                • 1. Deploy to Azure AI and cloud environments
                                  • 2. Implement logging, telemetry, and observability
                                    • 3. Optimize cost, latency, and throughput
                                      Topic 4: Design agentic AI solutions25%- Design agent architecture
                                      • 1. Design memory and state management
                                        • 2. Plan tool integration and orchestration
                                          • 3. Select agent patterns and topologies
                                            - Define requirements for agentic systems
                                            • 1. Define functional and non-functional requirements
                                              • 2. Identify use cases and scenarios
                                                • 3. Plan for responsible AI and governance

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q105-Q110):

                                                  NEW QUESTION # 105
                                                  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?

                                                  Answer: D

                                                  Explanation:
                                                  Edit mode is designed for targeted, developer-directed changes: you select the files in scope and describe the specific modification, giving you more granular control than the fully autonomous agent mode.


                                                  NEW QUESTION # 106
                                                  You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
                                                  You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
                                                  You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
                                                  What should you use?

                                                  Answer: D

                                                  Explanation:
                                                  The artifact.destroy audit log event records manual deletion of workflow artifacts. It provides the event data needed to identify the actor responsible for the deletion and the time at which it occurred.
                                                  Pull-request comments and descriptions can describe review activity, but they are not authoritative evidence of artifact deletion. Merge commits only establish repository-history changes and do not record deletion of workflow artifacts. A checks-related log event is unrelated to artifact lifecycle management.
                                                  Audit records should be correlated with the affected repository, workflow run, artifact name, and timestamp. This produces a traceable explanation of why evidence from a particular run is no longer present. Where long-term retention is required, the organization should combine artifact policies with external audit-log retention or streaming.
                                                  Study-guide topics: artifact accountability, audit events, evidence retention, and workflow investigation.


                                                  NEW QUESTION # 107
                                                  You use the GitHub Copilot CLI in a developer terminal.
                                                  You plan to use Copilot CLI programmatically to summarize recent commits.
                                                  You need to ensure that Copilot performs a low-risk, read-only action without granting Copilot broad write or execution capability.
                                                  Which Copilot CLI configuration should you use?

                                                  Answer: B

                                                  Explanation:
                                                  Option C is the intended answer because it grants a specific tool permission associated with Git rather than enabling every tool or allowing general file modification. The -p argument supplies the prompt programmatically, making the command suitable for a script that requests a summary of recent repository activity.
                                                  Option A grants unnecessarily broad tool access. Option B denies the Git capability needed to inspect commit history. Option D authorizes file-writing functionality, which is unrelated to producing a read-only summary.
                                                  The permission boundary nevertheless requires precise interpretation. Allowing Git is not inherently equivalent to enforcing read-only behavior: Git includes commands that modify files, repository state, and remote branches. A strict implementation should authorize only the required inspection commands, such as git log, using the permission-pattern syntax supported by the installed CLI version. The prompt should request summarization, but the prompt itself is not an access-control mechanism.
                                                  Therefore, C is the narrowest relevant selection among the supplied options, while command-specific restrictions are necessary to establish a genuinely read-only execution policy.
                                                  Relevant curriculum topics are selecting required tools, configuring tool permissions, and applying least privilege to agent execution.
                                                  Reference:


                                                  NEW QUESTION # 108
                                                  You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?

                                                  Answer: A

                                                  Explanation:
                                                  A is the intended answer among the supplied choices , because the required control is a Copilot-specific content exclusion mechanism rather than Git authorization or branch governance. The important technical concept is excluding the sensitive directory from Copilot ' s usable context and suggestions.
                                                  Current GitHub documentation implements this through Copilot Content exclusion settings at repository, organization, or enterprise scope. Administrators can specify exact files, directory paths, and patterns to exclude. For excluded content, GitHub states that inline suggestions are unavailable in the affected files, excluded file content does not inform suggestions elsewhere, and the files are excluded from supported Copilot responses and code review.
                                                  Neither CODEOWNERS nor branch protection prevents Copilot from reading or generating suggestions for a particular directory. CODEOWNERS establishes reviewers/ownership, while branch protections and rulesets govern repository operations such as pushes, merges, and review requirements. Those mechanisms operate too late in the lifecycle to satisfy a requirement concerning Copilot ' s access to sensitive file content.
                                                  For exam purposes, therefore, A maps to the content-exclusion concept . In current GitHub administration, implement the control through Settings # Copilot # Content exclusion and specify the sensitive directory path.
                                                  Study Guide Reference Topics: Implement Guardrails and Accountability; sensitive-context exclusion; least exposure; repository security boundaries.


                                                  NEW QUESTION # 109
                                                  You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
                                                  - agent-run that generates trace.json and plan.md
                                                  - review that waits for human approval before continuing
                                                  - deploy that uses the outputs from agent-run
                                                  You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
                                                  What should you do in the workflow?

                                                  Answer: D

                                                  Explanation:
                                                  1. Upload Artifacts (Job agent-run): Use the actions/upload-artifact action in your agent-run job to save trace.json and plan.md. This makes the files visible in the GitHub Actions UI and downloadable.
                                                  2. Configure Approval (Job review): Define an environment in your review job and configure Required Reviewers in your GitHub repository settings. The runner will pause until a human approves the run.
                                                  3. Download Artifacts (Job deploy): Use the actions/download-artifact action in the deploy job to retrieve the files uploaded by the agent-run job.
                                                  Reference:
                                                  https://docs.github.com/en/enterprise-server@3.20/admin/managing-github-actions-for-your-enterprise/getting-started-with-github-actions-for-your-enterprise/introducing-github-actions-to- your-enterprise


                                                  NEW QUESTION # 110
                                                  ......

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