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Microsoft GH-500 Exam Syllabus Topics:

Topic
Details

Topic 1

• 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 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

• 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 4

• 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 5

• 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.

 

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Microsoft GitHub Advanced Security Sample Questions (Q48-Q53):

NEW QUESTION # 48
What is the purpose of the SECURITY.md file in a GitHub repository?

• A. security.md
• B. contributing.md
• C. support.md
• D. readme.md

Answer: A

Explanation:
The correct place to look is the SECURITY.md file. This file provides contributors and security researchers with instructions on how to responsibly report vulnerabilities. It may include contact methods, preferred communication channels (e.g., security team email), and disclosure guidelines.
This file is considered a GitHub best practice and, when present, activates a "Report a vulnerability" button in the repository's Security tab.

 

NEW QUESTION # 49
What combination of security measures helps to mitigate risks throughout the SDLC (Software Development Life Cycle)?

• A. Automatically raise pull requests, which reduces your exposure to older versions of dependencies
• B. View alerts about dependencies that are known to contain security vulnerabilities
• C. Confidentially report security vulnerabilities and privately discuss and fix security vulnerabilities in your repository's code
• D. Search for potential security vulnerabilities, detect secrets, and show the full impact of changes to dependencies

Answer: D

Explanation:
These three features provide a complete layer of defense:
Code scanning identifies security flaws in your source code
Secret scanning detects exposed credentials
Dependency review shows the impact of package changes during a pull request Together, they give developers actionable insight into risk and coverage throughout the SDLC.

 

NEW QUESTION # 50
Which patterns are secret scanning validity checks available to?

• A. Push protection patterns
• B. Partner patterns
• C. Custom patterns
• D. High entropy strings

Answer: B

Explanation:
Validity checks - where GitHub verifies if a secret is still active - are available for partner patterns only. These are secrets issued by GitHub's trusted partners (like AWS, Slack, etc.) and have APIs for GitHub to validate token activity status.
Custom patterns and high entropy patterns do not support automated validity checks.

 

NEW QUESTION # 51
Where can you use CodeQL analysis for code scanning? (Each answer presents part of the solution. Choose two.)

• A. In the Files changed tab of the pull request
• B. In a workflow
• C. In an external continuous integration (CI) system
• D. In a third-party Git repository

Answer: B,C

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 tab in pull requests only shows diff - not analysis results.

 

NEW QUESTION # 52
As a developer with write access, you navigate to a code scanning alert in your repository. When will GitHub close this alert?

• A. After you find the code and click the alert within the pull request
• B. After you triage the pull request containing the alert
• C. When you use data-flow analysis to find potential security issues in code
• D. After you fix the code by committing within the pull request

Answer: D

Explanation:
GitHub automatically closes a code scanning alert when the vulnerable code is fixed in the same branch where the alert was generated, usually via a commit inside a pull request. Simply clicking or triaging an alert does not resolve it. The alert is re-evaluated after each push to the branch, and if the issue no longer exists, it is marked as resolved.

 

NEW QUESTION # 53
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