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Microsoft GH-500 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Advanced Security
Exam Number:GH-500
Certificate Validity Period:1 year
Exam Duration:120 minutes
Passing Score:700 out of 1000
Exam Price:$165 USD
Available Languages:English, Japanese, Spanish, Korean, Chinese (Simplified), French, German
Real Exam Qty:40-60
Related Certifications:GitHub Advanced Security Certification
Exam Format:Multiple choice, Drag and drop, Case study
Sample Questions:Microsoft GH-500 Sample Questions
Exam Way:Online (Proctored) or In-person at a testing center
Pre Condition:No mandatory prerequisites. Recommended to have experience with GitHub and basic understanding of security concepts.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-advanced-security/

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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.
Topic 5
  • 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.

Microsoft GitHub Advanced Security Sample Questions (Q70-Q75):

NEW QUESTION # 70
Which security feature shows a vulnerable dependency in a pull request?

Answer: B


NEW QUESTION # 71
A dependency has a known vulnerability. What does the warning message include?

Answer: D

Explanation:
When a vulnerability is detected, GitHub shows a warning that includes a brief description of the vulnerability. This typically covers the name of the CVE (if available), a short summary of the issue, severity level, and potential impact. The message also links to additional advisory data from the GitHub Advisory Database.
This helps developers understand the context and urgency of the vulnerability before applying the fix.


NEW QUESTION # 72
Where can you use CodeQL analysis for code scanning? Each answer presents part of the solution. (Choose two.)

Answer: A,D

Explanation:
In a workflow: GitHub Actions workflows are the most common place for CodeQL code scanning.
The codeql-analysis.yml defines how the analysis runs and when it triggers.
In an external CI system: GitHub allows you to run CodeQL analysis outside of GitHub Actions.
Once complete, the results can be uploaded using the upload-sarif action to make alerts visible in the repository.
You cannot run or trigger analysis from third-party repositories directly, and the Files changed tabin pull requests only shows diff - not analysis results.


NEW QUESTION # 73
As a developer, you need to configure a code scanning workflow for a repository where GitHub Advanced Security is enabled. What minimum repository permission do you need?

Answer: B

Explanation:
To create or modify a code scanning workflow file (typically under .github/workflows/codeql-analysis.yml), you must have Write access to the repository.
Write permission allows you to commit the workflow file, which is required to run or configure code scanning using GitHub Actions.
: GitHub Docs - Repository Permissions and Workflow Setup


NEW QUESTION # 74
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: D

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 # 75
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