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Microsoft GH-500 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GH-500: GitHub Advanced Security
Exam Number:GH-500
Passing Score:700 / 1000
Exam Duration:100 minutes
Real Exam Qty:40โ€“60
Available Languages:Arabic (Saudi Arabia), Japanese, Spanish, Korean, Chinese (Simplified), English, French, Portuguese (Brazil), German
Exam Price:$99 USD
Exam Format:Multiple-choice, Scenario-based, Interactive tasks
Certificate Validity Period:1 year
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

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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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.

Microsoft GitHub Advanced Security Sample Questions (Q94-Q99):

NEW QUESTION # 94
Which of the following formats are used to describe a Dependabot alert? Each answer presents a complete solution. (Choose two.)

Answer: A,C

Explanation:
Dependabot alerts utilize standardized identifiers to describe vulnerabilities:
CVE (Common Vulnerabilities and Exposures):A widely recognized identifier for publicly known cybersecurity vulnerabilities.
CWE (Common Weakness Enumeration):A category system for software weaknesses and vulnerabilities.
These identifiers help developers understand the nature of the vulnerabilities and facilitate the search for more information or remediation strategies.
Note:
Dependabot alerts utilize standardized identifiers like Common Vulnerabilities and Exposures (CVE) identifiers and GitHub Advisory IDs to describe vulnerabilities within your project's dependencies. These identifiers help link the specific vulnerability to a standardized database entry, providing more context and details about the issue.
Publicly disclosed CWEs used by the Dismiss low impact issues for development-scoped dependencies rule Along with the ecosystem:npm and scope:development alert metadata, we use the following GitHub-curated Common Weakness Enumerations (CWEs) to filter out low impact alerts for the Dismiss low impact issues for development-scoped dependencies rule. We regularly improve this list and vulnerability patterns covered by built-in rules.
Resource Management Issues
CWE-400 Uncontrolled Resource Consumption
CWE-770 Allocation of Resources Without Limits or Throttling
Etc.
Incorrect:
[Not A] Dependabot alerts, combined with Vulnerability Exploitability eXchange (VEX), help users understand and manage vulnerabilities in their dependencies. Dependabot provides alerts when vulnerable dependencies are found, and VEX adds context about whether those vulnerabilities are actually exploitable in a specific environment.
VEX (Vulnerability Exploitability eXchange):
Purpose:
VEX provides a standardized way to communicate whether a vulnerability is actually exploitable in a specific context, like a particular product or environment.
Functionality:
VEX can be used to convey that a vulnerability doesn't pose a risk in a specific scenario, potentially due to specific configurations or mitigations.
Example:
If a product uses a vulnerable component, but that component is not reachable or has a mitigation in place, VEX can be used to communicate that the vulnerability is not exploitable.
[Not D]
Dependabot helps users focus on the most important alerts by including EPSS scores that indicate likelihood of exploitation, now generally available [February 2025] Dependabot alerts now feature the Exploit Prediction Scoring System (EPSS) from the global Forum of Incident Response and Security Teams (FIRST), helping you better assess vulnerability risks.
EPSS scores predict the likelihood of a vulnerability being exploited, with scores ranging from 0 to
1 (0 to 100%). Higher scores mean higher risk. We also show the EPSS score percentile, indicating how a vulnerability compares to others.
For example, a 90.534% EPSS score at the 95th percentile means:
90.534% chance of exploitation in the next 30 days
95% of other vulnerabilities are less likely to be exploited
You can use EPSS scores to help prioritize dependency vulnerabilities based on exploit likelihood.


NEW QUESTION # 95
After defining a secret scanning custom pattern, what is the final step before publishing the pattern?

Answer: A

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 # 96
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 # 97
How would you build your code within the CodeQL analysis workflow? (Each answer presents a complete solution. Choose two.)

Answer: A,E

Explanation:
Comprehensive and Detailed Explanation:
When setting up CodeQL analysis for compiled languages, there are two primary methods to build your code:
GitHub Docs
Autobuild: CodeQL attempts to automatically build your codebase using the most likely build method. This is suitable for standard build processes.
GitHub Docs
Custom Build Steps: For complex or non-standard build processes, you can implement custom build steps by specifying explicit build commands in your workflow. This provides greater control over the build process.
GitHub Docs
The init action initializes the CodeQL analysis but does not build the code. The jobs.analyze.runs-on specifies the operating system for the runner but is not directly related to building the code. Uploading compiled binaries is not a method supported by CodeQL for analysis.


NEW QUESTION # 98
You have a GitHub Enterprise Cloud Organization that contains a private repository named Repo1 and has GitHub Secret Protection enabled. Repo1 contains a workflow that writes an access key ID to config.txt and a secret access key to secrets.txt. Repo1 has secret scanning push protection enabled.
You discover that a developer at your company was able to push changes that contain both the access key ID and the secret access key without the push being blocked.
What is the cause of the issue?

Answer: D

Explanation:
Secret validation rules: GitHub's secret scanning push protection for AWS credentials checks for a high-confidence pair (the Access Key ID and Secret Access Key) appearing together within the same file or immediate context to prevent false positives.
Separation bypasses detection: Because the developer placed the Access Key ID in config.txt and the Secret Access Key in secrets.txt, the push protection pattern matching did not recognize them as a coupled, valid secret pair, allowing the push to succeed.
Incorrect:
[Not B] The push did NOT include a merge into the default branch:
This is incorrect because push protection scans all pushes to any branch within the repository, not just the default branch, to prevent secrets from entering the commit history anywhere.
[Not C] The push was performed on a private repository:
This is incorrect because GitHub Enterprise Cloud organizations can enable secret scanning and push protection for both public and private repositories alike.
Reference:
https://rogierdijkman.medium.com/privilege-escalation-via-storage-accounts-bca24373cc2e


NEW QUESTION # 99
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