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Microsoft GH-500 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Microsoft GitHub Advanced Security (GH-500)
Exam Number:	GH-500
Real Exam Qty:	40-60 (approx.)
Certificate Validity Period:	1 year (renewable via Microsoft certification renewal)
Exam Price:	$165 USD (may vary by region)
Available Languages:	English
Exam Format:	Case studies, Multiple response, Scenario-based questions, Multiple choice
Related Certifications:	Microsoft DevOps Engineer Expert GitHub Administration GitHub Actions
Exam Duration:	120 minutes
Passing Score:	700 (on a scale of 1000)
Recommended Training:	Microsoft Learn - GitHub Advanced Security GitHub Advanced Security Documentation
Exam Registration:	Microsoft Certification Exam Page Pearson VUE Microsoft Exams
Sample Questions:	Microsoft GH-500 Sample Questions
Exam Way:	Online proctored exam or authorized testing center (Pearson VUE)
Pre Condition:	Recommended: familiarity with GitHub, DevSecOps practices, and CI/CD pipelines. No formal prerequisites required.
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/exams/gh-500/

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Microsoft GH-500 Exam Syllabus Topics:

Topic	Details
Topic 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.
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 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 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 (Q126-Q131):

NEW QUESTION # 126
Assuming that notification and alert recipients are not customized, what does GitHub do when it identifies a vulnerable dependency in a repository where Dependabot alerts are enabled? (Each answer presents part of the solution. Choose two.)

A. It generates Dependabot alerts by default for all private repositories.
B. It notifies the repository administrators about the new alert.
C. It generates a Dependabot alert and displays it on the Security tab for the repository.
D. It consults with a security service and conducts a thorough vulnerability review.

Answer: B,C

Explanation:
When GitHub identifies a vulnerable dependency in a repository with Dependabot alerts enabled, it performs the following actions:
[A] Geneates a Dependabot alert: The alert is displayed on the repository's Security tab, providing details about the vulnerability and affected dependency.
[D] Notifies repository maintainers: By default, GitHub notifies users with write, maintain, or admin permissions about new Dependabot alerts.
These actions ensure that responsible parties are informed promptly to address the vulnerability.

NEW QUESTION # 127
Assuming that no custom Dependabot behavior is configured, who has the ability to merge a pull request created via Dependabot security updates?

A. an enterprise administrator
B. a repository member of an enterprise organization
C. a user who has read access to the repository
D. a user who has write access to the repository

Answer: D

Explanation:
Managing pull requests for dependency updates
You manage pull requests raised by Dependabot in much the same way as other pull requests, but there are some extra options.
Who can use this feature?
Users with write access

NEW QUESTION # 128
Where in the repository can you give additional users access to secret scanning alerts?

A. Secrets
B. Security
C. Insights
D. Settings

Answer: D

Explanation:
To grant specific users access to view and manage secret scanning alerts , you do this via the Settings tab of the repository. From there, under the "Code security and analysis" section, you can add individuals or teams with roles such as security manager .
The Security tab only displays alerts; access control is handled in Settings.
: GitHub Docs - Granting Access to Secret Scanning Alerts

NEW QUESTION # 129
What does code scanning do?

A. It scans your entire Git history on branches present in your GitHub repository for any secrets
B. It prevents code pushes with vulnerabilities as a pre-receive hook
C. It contacts maintainers to ask them to create security advisories if a vulnerability is found
D. It analyzes a GitHub repository to find security vulnerabilities

Answer: D

Explanation:
Code scanning is a static analysis feature that examines your source code to identify security vulnerabilities and coding errors . It runs either on every push, pull request, or a scheduled time depending on the workflow configuration.
It does not automatically contact maintainers, scan full Git history, or block pushes unless explicitly configured to do so.
: GitHub Docs - About Code Scanning

NEW QUESTION # 130
You are configuring a CodeQL workflow for compiled languages. What happens if your workflow uses a language matrix?

A. Analysis of other languages in your repository will fail unless you supply explicit build commands.
B. Autobuild attempts to build the supported language that has the most source files in the repository.
C. You may need to install additional software to use the autobuild process.
D. Autobuild attempts to build each of the languages listed in the matrix.

Answer: D

Explanation:
If your workflow uses a language matrix, autobuild attempts to build each of the compiled languages listed in the matrix. Without a matrix autobuild attempts to build the supported compiled language that has the most source files in the repository. With the exception of Go, analysis of other compiled languages in your repository will fail unless you supply explicit build commands.
Note:
CodeQL build modes
The CodeQL action supports three different build modes for compiled languages:
none - the CodeQL database is created directly from the codebase without building the codebase (supported for all interpreted languages, and additionally supported for C/C++, C# and Java).
autobuild - CodeQL detects the most likely build method and uses this to attempt to build the codebase and create a database for analysis (supported for all compiled languages).
manual - you define the build steps to use for the codebase in the workflow (supported for all compiled languages, except Rust).

NEW QUESTION # 131
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