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Microsoft GH-500 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft GitHub Advanced Security (GH-500)
Exam Number:GH-500
Exam Duration:120 minutes
Exam Format:Scenario-based questions, Multiple choice, Case studies, Multiple response
Related Certifications:GitHub Administration
GitHub Actions
Microsoft DevOps Engineer Expert
Real Exam Qty:40-60 (approx.)
Passing Score:700 (on a scale of 1000)
Available Languages:English
Certificate Validity Period:1 year (renewable via Microsoft certification renewal)
Exam Price:$165 USD (may vary by region)
Recommended Training:GitHub Advanced Security Documentation
Microsoft Learn - GitHub Advanced Security
Exam Registration:Microsoft Certification Exam Page
Pearson VUE Microsoft Exams
Sample Questions:Microsoft GH-500 Sample Questions
Exam Way:Online proctored exam or authorized testing center (Pearson VUE)
Pre Condition:Recommended: familiarity with GitHub, DevSecOps practices, and CI/CD pipelines. No formal prerequisites required.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/gh-500/

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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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
  • 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.

Microsoft GitHub Advanced Security Sample Questions (Q90-Q95):

NEW QUESTION # 90
What should you do after receiving an alert about a dependency added in a pull request?

Answer: C

Explanation:
If an alert is raised on a pull request dependency, best practice is to update the dependency to a secure version before merging the PR. This prevents the vulnerable version from entering the main codebase.
Merging or deploying the PR without fixing the issue exposes your production environment to known risks.


NEW QUESTION # 91
Why should you dismiss a code scanning alert?

Answer: D

Explanation:
You should dismiss a code scanning alert if the flagged code is not a true security concern, such as:
*-> Code in test files
Code paths that are unreachable or safe by design
False positives from the scanner
Fixing the code would automatically resolve the alert - not dismiss it. Dismissing is for valid exceptions or noise reduction.


NEW QUESTION # 92
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: A

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 # 93
Which of the following Watch settings could you use to get Dependabot alert notifications? (Each answer presents part of the solution. Choose two.)

Answer: B,C

Explanation:
Comprehensive and Detailed Explanation:
To receive Dependabot alert notifications for a repository, you can utilize the following Watch settings:
Custom setting: Allows you to tailor your notifications, enabling you to subscribe specifically to security alerts, including those from Dependabot.
All Activity setting: Subscribes you to all notifications for the repository, encompassing issues, pull requests, and security alerts like those from Dependabot.
The Participating and @mentions setting limits notifications to conversations you're directly involved in or mentioned, which may not include security alerts. The Ignore setting unsubscribes you from all notifications, including critical security alerts.
GitHub Docs
+1
GitHub Docs
+1


NEW QUESTION # 94
You are tasked with filtering queries in a CodeQL query suite. Which metadata tag matches on the last path component?

Answer: D

Explanation:
In a CodeQL query suite, the query filename constraint matches the final path component-that is, the filename of the query file itself. GitHub distinguishes this from query path, which matches the path to the query relative to the enclosing CodeQL pack. The tags contain and tags contain all constraints operate on values from the query's @tags metadata rather than its filesystem path. tags contain requires one of the specified strings to match a tag component, while tags contain all requires all specified strings to be represented. Therefore, when the objective is specifically to filter queries according to the last component of their file path, query filename is the appropriate CodeQL query-suite constraint.


NEW QUESTION # 95
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