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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Describe GitHub Advanced Security best practices, results, and how to take corrective measures: This section evaluates skills of Security Managers and Development Team Leads in effectively handling GHAS results and applying best practices. It includes using Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) identifiers to describe alerts and suggest remediation, decision-making processes for closing or dismissing alerts including documentation and data-based decisions, understanding default CodeQL query suites, how CodeQL analyzes compiled versus interpreted languages, the roles and responsibilities of development and security teams in workflows, adjusting severity thresholds for code scanning pull request status checks, prioritizing secret scanning remediation with filters, enforcing CodeQL and Dependency Review workflows via repository rulesets, and configuring code scanning, secret scanning, and dependency analysis to detect and remediate vulnerabilities earlier in the development lifecycle, such as during pull requests or by enabling push protection.
Topic 2
  • Configure and use Code Scanning with CodeQL: This domain measures skills of Application Security Analysts and DevSecOps Engineers in code scanning using both CodeQL and third-party tools. It covers enabling code scanning, the role of code scanning in the development lifecycle, differences between enabling CodeQL versus third-party analysis, implementing CodeQL in GitHub Actions workflows versus other CI tools, uploading SARIF results, configuring workflow frequency and triggering events, editing workflow templates for active repositories, viewing CodeQL scan results, troubleshooting workflow failures and customizing configurations, analyzing data flows through code, interpreting code scanning alerts with linked documentation, deciding when to dismiss alerts, understanding CodeQL limitations related to compilation and language support, and defining SARIF categories.
Topic 3
  • Describe the GHAS security features and functionality: This section of the exam measures skills of Security Engineers and Software Developers and covers understanding the role of GitHub Advanced Security (GHAS) features within the overall security ecosystem. Candidates learn to differentiate security features available automatically for open source projects versus those unlocked when GHAS is paired with GitHub Enterprise Cloud (GHEC) or GitHub Enterprise Server (GHES). The domain includes knowledge of Security Overview dashboards, the distinctions between secret scanning and code scanning, and how secret scanning, code scanning, and Dependabot work together to secure the software development lifecycle. It also covers scenarios contrasting isolated security reviews with integrated security throughout the development lifecycle, how vulnerable dependencies are detected using manifests and vulnerability databases, appropriate responses to alerts, the risks of ignoring alerts, developer responsibilities for alerts, access management for viewing alerts, and the placement of Dependabot alerts in the development process.
Topic 4
  • Configure and use secret scanning: This domain targets DevOps Engineers and Security Analysts with the skills to configure and manage secret scanning. It includes understanding what secret scanning is and its push protection capability to prevent secret leaks. Candidates differentiate secret scanning availability in public versus private repositories, enable scanning in private repos, and learn how to respond appropriately to alerts. The domain covers alert generation criteria for secrets, user role-based alert visibility and notification, customizing default scanning behavior, assigning alert recipients beyond admins, excluding files from scans, and enabling custom secret scanning within repositories.
Topic 5
  • Configure and use Dependabot and Dependency Review: Focused on Software Engineers and Vulnerability Management Specialists, this section describes tools for managing vulnerabilities in dependencies. Candidates learn about the dependency graph and how it is generated, the concept and format of the Software Bill of Materials (SBOM), definitions of dependency vulnerabilities, Dependabot alerts and security updates, and Dependency Review functionality. It covers how alerts are generated based on the dependency graph and GitHub Advisory Database, differences between Dependabot and Dependency Review, enabling and configuring these tools in private repositories and organizations, default alert settings, required permissions, creating Dependabot configuration files and rules to auto-dismiss alerts, setting up Dependency Review workflows including license checks and severity thresholds, configuring notifications, identifying vulnerabilities from alerts and pull requests, enabling security updates, and taking remediation actions including testing and merging pull requests.

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Microsoft GitHub Advanced Security Sample Questions (Q50-Q55):

NEW QUESTION # 50
What is the format of the GitHub security advisory form?

Answer: C

Explanation:
GitHub states that its repository security advisory form is a standardized form that matches the Common Vulnerabilities and Exposures (CVE) description format. GitHub Security Advisories build on the CVE ecosystem, and GitHub itself is a CVE Numbering Authority capable of assigning CVE identification numbers for eligible vulnerabilities. The advisory form captures structured information such as the vulnerability description, affected products, vulnerable and patched versions, severity, CWEs, references, and CVE information. A CNA is an organization authorized to assign CVEs rather than a separate advisory- description format. Dependabot alerts are generated for vulnerable dependencies and are unrelated to the structure of the security advisory form. Therefore, the CVE description format is the correct choice.


NEW QUESTION # 51
What filter or sort settings can be used to prioritize the secret scanning alerts that present the most risk?

Answer: D

Explanation:
The best way to prioritize secret scanning alerts is to filter by active secrets - these are secrets GitHub has confirmed are still valid and could be exploited. This allows security teams to focus on high-risk exposures that require immediate attention.
Sorting by time or filtering by custom patterns won't help with risk prioritization directly.
: GitHub Docs - Filtering Secret Scanning Alerts


NEW QUESTION # 52
You are managing code scanning alerts for your repository. You receive an alert highlighting a problem with data flow. What do you click for additional context on the alert?

Answer: B

Explanation:
When dealing with a data flow issue in a code scanning alert, clicking on "Show paths" provides a detailed view of the data's journey through the code. This includes the source of the data, the path it takes, and where it ends up (the sink). This information is crucial for understanding how untrusted data might reach sensitive parts of your application and helps in identifying where to implement proper validation or sanitization.


NEW QUESTION # 53
When using code scanning and GitHub Actions on Windows, what is the relative difference between the minute consumption of code scanning jobs and jobs on Linux runners?

Answer: C

Explanation:
Under the GitHub Actions minute-multiplier model tested by this question, jobs executed on GitHub-hosted Windows runners consume included minutes at twice the rate of equivalent Linux-runner jobs. Linux has a multiplier of 1, while Windows has a multiplier of 2; historically macOS used a substantially higher multiplier. Because CodeQL advanced setup can execute as a GitHub Actions workflow, the operating system selected with runs-on affects Actions consumption in the same way as other GitHub-hosted workflows.
GitHub's newer billing documentation increasingly expresses runner usage through operating-system-specific per-minute pricing rather than only multiplier terminology, but the exam question specifically tests the established Windows-versus-Linux minute multiplier. Therefore, the expected answer is 2x higher.


NEW QUESTION # 54
Which of the following options would close a Dependabot alert?

Answer: D

Explanation:
To close a Dependabot alert, the primary method is to address the underlying vulnerability. This usually involves merging a pull request that Dependabot creates to update the vulnerable dependency or manually fixing the issue and pushing the updated code. Once the vulnerability is resolved, the alert will be automatically closed. Alternatively, you can manually close alerts in the
"Security" tab of your repository.
Here's a more detailed breakdown:
1. Addressing the Vulnerability:
*-> Merge Dependabot Pull Requests:
If Dependabot has identified a vulnerability and created a pull request to fix it, review and merge the pull request. This will automatically update the dependency and close the alert.
* Manual Fix:
If you prefer to fix the vulnerability yourself, make the necessary code changes to update the dependency. Once the changes are pushed and merged, Dependabot will recognize the fix and close the alert.
2. Manually Closing Alerts (if needed)


NEW QUESTION # 55
......

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