Microsoft GH-500 Prüfung Übungen und Antworten

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Microsoft GH-500 Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 2
  • 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.
Thema 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.
Thema 4
  • 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.
Thema 5
  • 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.

>> GH-500 Fragenkatalog <<

Microsoft GH-500 Simulationsfragen, GH-500 Prüfungsvorbereitung

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Microsoft GitHub Advanced Security GH-500 Prüfungsfragen mit Lösungen (Q114-Q119):

114. Frage
The autobuild step in the CodeQL workflow has failed. What should you do?

Antwort: D

Begründung:
If auto build fails (which attempts to automatically detect how to build your project), you shoulddisable itin your workflow andreplace it with explicit build commands, using steps like run:
make or run: ./gradlew build.
This ensures CodeQL can still extract and analyze the code correctly.


115. Frage
A colleague ignores a code scanning alert. What are the implications of the colleague's action?
Each answer presents part of the solution. (Choose three.)

Antwort: B,D,E

Begründung:
If you configure code scanning using CodeQL, you can also find data-flow problems in your code.
Data-flow analysis finds potential security issues in code, such as: using data insecurely[C], passing dangerous arguments to functions [D], and leaking sensitive information[B].
When code scanning reports data-flow alerts, GitHub shows you how data moves through the code. Code scanning allows you to identify the areas of your code that leak sensitive information, and that could be the entry point for attacks by malicious users.


116. Frage
Where can you use CodeQL analysis for code scanning? Each answer presents part of the solution. (Choose two.)

Antwort: C,D

Begründung:
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.


117. Frage
Where can a user change a repository's code scanning severity threshold that fails a pull request status check?

Antwort: B

Begründung:
Code scanning merge requirements and severity thresholds are repository configuration controls and are therefore managed from the repository's Settings area. In GitHub's current interface, administrators can create a branch ruleset under Settings and configure Require code scanning results. They can then select the code scanning tool and specify alert thresholds, including which normal alerts or security-severity levels should prevent merging. The Security and quality tab is primarily where security findings are reviewed and triaged, while the Actions tab displays workflow execution information. The Pull Requests tab is used to review and merge proposed changes rather than configure repository-wide code scanning policy. Thus, although GitHub's implementation has evolved toward ruleset-based merge protection, the appropriate top-level location remains the repository's Settings tab.


118. Frage
After defining a secret scanning custom pattern, what is the final step before publishing the pattern?

Antwort: B

Begründung:
Defining a custom pattern for a repository [See step 6 below]
Before defining a custom pattern, you must ensure that Secret Protection is enabled on your repository.
1. On GitHub, navigate to the main page of the repository.
2. Under your repository name, click Settings. If you cannot see the "Settings" tab, select the dropdown menu, then click Settings.
3. In the "Security" section of the sidebar, click Advanced Security.
4. Under "Secret Protection", to the right of "Custom patterns", click New pattern.
5. Enter the details for your new custom pattern. You must at least provide the name for your pattern, and a regular expression for the format of your secret pattern.
*-> 6. When you're ready to test your new custom pattern, to identify matches in the repository without creating alerts, click Save and dry run.
7. When the dry run finishes, you'll see a sample of results (up to 1000). Review the results and identify any false positive results.

8. Edit the new custom pattern to fix any problems with the results, then, to test your changes, click Save and dry run.
9. When you're satisfied with your new custom pattern, click Publish pattern.
10. Optionally, to enable push protection for your custom pattern, click Enable.


119. Frage
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