Free PDF 2026 Microsoft GH-500–High-quality Reliable Exam Syllabus

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Microsoft GH-500 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GH-500: GitHub Advanced Security
Exam Number:GH-500
Available Languages:Japanese, Chinese (Simplified), Korean, Arabic (Saudi Arabia), Spanish, German, English, Portuguese (Brazil), French
Exam Format:Scenario-based, Multiple-choice, Interactive tasks
Certificate Validity Period:1 year
Passing Score:700 / 1000
Exam Duration:100 minutes
Exam Price:$99 USD
Real Exam Qty:40–60
Recommended Training:Course GH-500T00: GitHub Advanced Security
GitHub Advanced Security Learning Path
Exam Registration:Microsoft Learn Exam Registration
Pearson VUE Scheduling
Sample Questions:Microsoft GH-500 Sample Questions
Exam Way:Online proctored or onsite testing via Pearson VUE
Pre Condition:Familiarity with GitHub fundamentals, CI/CD pipelines, and secure development practices; no mandatory prerequisite exams
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/gh-500

>> GH-500 Reliable Exam Syllabus <<

Free PDF Quiz GH-500 - GitHub Advanced Security –Reliable Reliable Exam Syllabus

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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
  • 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 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 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
  • 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 (Q102-Q107):

NEW QUESTION # 102
Where can you view code scanning results from CodeQL analysis?

Answer: D

Explanation:
You can use CodeQL to identify vulnerabilities and errors in your code. The results are shown as code scanning alerts in GitHub.
Note:
Viewing the alerts for a repository
You need write permission to view a summary of all the alerts for a repository on the Security tab.
By default, the code scanning alerts page is filtered to show alerts for the default branch of the repository only.
1. On GitHub, navigate to the main page of the repository.
2. Under the repository name, click Security. If you cannot see the "Security" tab, select the dropdown menu, and then click Security.
3. In the left sidebar, click Code scanning.
4. Optionally, use the free text search box or the dropdown menus to filter alerts. For example, you can filter by the tool that was used to identify alerts.

5. Etc.


NEW QUESTION # 103
After investigating a code scanning alert related to injection, you determine that the input is properly sanitized using custom logic. What should be your next step?

Answer: C

Explanation:
When you identify that a code scanning alert is a false positive-such as when your code uses a custom sanitization method not recognized by the analysis-you should dismiss the alert with the reason "false positive." This action helps improve the accuracy of future analyses and maintains the relevance of your security alerts.
As per GitHub's documentation:
"If you dismiss a CodeQL alert as a false positive result, for example because the code uses a sanitization library that isn't supported, consider contributing to the CodeQL repository and improving the analysis." By dismissing the alert appropriately, you ensure that your codebase's security alerts remain actionable and relevant.


NEW QUESTION # 104
Where can you use CodeQL analysis for code scanning? (Each answer presents part of the solution. Choose two.)

Answer: B,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 tab in pull requests only shows diff - not analysis results.


NEW QUESTION # 105
You are managing code scanning alerts for your repository. You receive an alert highlighting a problem with data flow. What do you click for additional context on the alert?

Answer: B

Explanation:
When dealing with a data flow issue in a code scanning alert, clicking on "Show paths" provides a detailed view of the data's journey through the code. This includes the source of the data, the path it takes, and where it ends up (the sink). This information is crucial for understanding how untrusted data might reach sensitive parts of your application and helps in identifying where to implement proper validation or sanitization.
: GitHub Docs - Assessing code scanning alerts for your repository GitHub Docs+1GitHub Docs+1


NEW QUESTION # 106
After defining a secret scanning custom pattern, what is the final step before publishing the pattern?

Answer: B

Explanation:
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.


NEW QUESTION # 107
......

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