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Microsoft GH-500 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.
Topic 4	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 5	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.

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Microsoft GitHub Advanced Security Sample Questions (Q103-Q108):

NEW QUESTION # 103
What filter or sort settings can be used to prioritize the secret scanning alerts that present the most risk?

A. Sort to display the oldest first
B. Select only the custom patterns
C. Filter to display active secrets
D. Sort to display the newest first

Answer: C

Explanation:
The best way to prioritize secret scanning alerts is to filter by active secrets - these are secrets GitHub has confirmed are still valid and could be exploited. This allows security teams to focus on high-risk exposures that require immediate attention.
Sorting by time or filtering by custom patterns won't help with risk prioritization directly.
: GitHub Docs - Filtering Secret Scanning Alerts

NEW QUESTION # 104
How would you build your code within the CodeQL analysis workflow? (Each answer presents a complete solution. Choose two.)

A. Use CodeQL's init action.
B. Use jobs.analyze.runs-on.
C. Ignore paths.
D. Implement custom build steps.
E. Upload compiled binaries.
F. Use CodeQL's autobuild action.

Answer: D,F

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.
References: GitHub Docs - CodeQL code scanning for compiled languages

NEW QUESTION # 105
After defining a secret scanning custom pattern, what is the final step before publishing the pattern?

A. Enabling push protection
B. Performing a dry run
C. Adding additional match requirements
D. Defining a custom pattern

Answer: B

Explanation:
GitHub requires you to perform a dry run before publishing a new secret scanning custom pattern. After defining the pattern and providing a sample test string, you select Save and dry run. GitHub searches the relevant repository data without creating secret scanning alerts and returns a sample of matching results. You should review these results for false positives, adjust the regular expression or additional match requirements if necessary, and repeat the dry run until the pattern behaves as intended. Only after a successful dry run and review should you publish the custom pattern. Push protection is optional and is enabled after the pattern has been published; GitHub explicitly notes that the option is not available until the dry run succeeds and the pattern is published.

NEW QUESTION # 106
Which details do you have to provide to create a custom pattern for secret scanning? (Each answer presents part of the solution. Choose two.)

A. The secret format
B. The name of the pattern
C. Additional match requirements for the secret format
D. A list of repositories to scan

Answer: A,B

Explanation:
When defining a custom pattern for secret scanning, two key fields are required:
Name of the pattern: A unique label to identify the pattern
Secret format: A regular expression that defines what the secret looks like (e.g., token format) You can optionally specify additional match requirements (like required context keywords), but they're not mandatory. Listing repositories is also not part of the required fields during pattern creation.

NEW QUESTION # 107
Assuming there is no custom Dependabot behavior configured, where possible, what does Dependabot do after sending an alert about a vulnerable dependency in a repository?

A. Constructs a graph of all the repository's dependencies and public dependents for the default branch
B. Creates a pull request to upgrade the vulnerable dependency to the minimum possible secure version
C. Scans any push to all branches and generates an alert for each vulnerable repository
D. Scans repositories for vulnerable dependencies on a schedule and adds those files to a manifest

Answer: B

Explanation:
After generating an alert for a vulnerable dependency, Dependabot automatically attempts to create a pull request to upgrade that dependency to the minimum required secure version-if a fix is available and compatible with your project.
This automated PR helps teams fix vulnerabilities quickly with minimal manual intervention. You can also configure update behaviors using dependabot.yml, but in the default state, PR creation is automatic.

NEW QUESTION # 108
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