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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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 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 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 (Q27-Q32):

NEW QUESTION # 27
What is the best way to ensure that added dependencies' licenses are checked and new code is analyzed at the repository level?

Answer: D

Explanation:
A GitHub Dependabot Dependency Review workflow can ensure added dependencies' licenses are checked and new code is analyzed at the repository level by integrating a Software Composition Analysis (SCA) tool into a CI/CD pipeline, which scans pull requests for dependency changes and potential security or license issues before code is merged. This proactive approach helps maintain code quality and compliance by automatically flagging vulnerabilities and invalid licenses, with the option to block merges based on severity.
Note:
1. Integrate into Workflow: You add a job for the Dependency Review action to your CI workflow file within the .github/workflows directory.
2. Scan Pull Requests: The action automatically scans the changes in your pull requests to identify new, removed, or updated dependencies.
3. Analyze Dependencies: It then analyzes these dependencies for known security vulnerabilities and license compliance issues.
4. Flag Issues: The results, including any security alerts and license information, are displayed in the job logs and as a job summary beneath the action run.
5. Enforce and Mitigate Risks: You can configure the action to automatically fail the build and block merges for high-severity issues, ensuring that only compliant and secure code is added to your project.


NEW QUESTION # 28
Which key is required in the update settings of the Dependabot configuration file?

Answer: D

Explanation:
In a dependabot.yml configuration file, package-ecosystem is a required key . It defines the package manager being used in that update configuration (e.g., npm, pip, maven, etc.).
Without this key, Dependabot cannot determine how to analyze or update dependencies. Other keys like rebase-strategy or commit-message are optional and used for customizing behavior.
: GitHub Docs - Dependabot Configuration Options


NEW QUESTION # 29
Which of the following is the best way to prevent developers from adding secrets to the repository?

Answer: C

Explanation:
Enabling push protection for your repository
With push protection, secret scanning blocks contributors from pushing secrets to a repository and generates an alert whenever a contributor bypasses the block.


NEW QUESTION # 30
If notification and alert recipients are not customized, which users receive notifications about new Dependabot alerts in an affected repository?

Answer: A

Explanation:
By default, users with Write, Maintain, or Admin permissions will receive notifications for new Dependabot alerts . However, Write permission is the minimum level needed to be automatically notified.
Users with only Read access do not receive alerts unless added explicitly.: GitHub Docs - Dependabot Alerts Notification Scope


NEW QUESTION # 31
When using CodeQL, how does extraction for compiled languages work?

Answer: D

Explanation:
For compiled languages, CodeQL performs extraction bymonitoring the normal build process.
This means it watches your usual build commands (like make, javac, or dotnet build) and extracts the relevant data from the actual build steps being executed. CodeQL uses this information to construct a semantic database of the application.
This approach ensures that CodeQL captures a precise, real-world representation of the code and its behavior as it is compiled, including platform-specific configurations or conditional logic used during build.


NEW QUESTION # 32
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