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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
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 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
  • 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.

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Microsoft GitHub Advanced Security Sample Questions (Q76-Q81):

NEW QUESTION # 76
Which of the following is required to block the merge of a pull request containing critical vulnerabilities? Each correct answer presents part of the solution. (Choose two.)

Answer: A,C

Explanation:
Set code scanning merge protection
You can use rulesets to set code scanning merge protection for pull requests.
You can use rulesets to prevent pull requests from being merged when one of the following conditions is met:
A required tool found a code scanning alert of a severity that is defined in a ruleset.
A required code scanning tool's analysis is still in progress.
A required code scanning tool is not configured for the repository.
Note:
Creating a merge protection ruleset for a 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 left sidebar, under "Code and automation," click Rules, then click Rulesets.
4. Click New ruleset.
5. To create a ruleset targeting branches, click New branch ruleset.
6. Under "Ruleset name," type a name for the ruleset.
7. Optionally, to change the default enforcement status, click Disabled and select an enforcement status.
8. Under "Branch protections", select Require code scanning results.
9. Under "Required tools and alert thresholds", click Add tool and select a code scanning tool with the dropdown. For example, "CodeQL".
10. Next to the name of a code scanning tool:
Click Alerts and select one of: None, Errors, Errors and Warnings or All.
Click Security alerts and select one of: None, Critical, High or higher, Medium or higher, or All.


NEW QUESTION # 77
Which Dependabot configuration fields are required? Each answer presents part of the solution.
(Choose three.)

Answer: A,D,E

Explanation:
When configuring Dependabot via the dependabot.yml file, the following fields are mandatory for each update configuration:
[D] directory: Specifies the location of the package manifest within the repository. This tellsDependabot where to look for dependency files.
[A] package-ecosystem: Indicates the type of package manager (e.g., npm, pip, maven) used in the specified directory.
[C] schedule.interval: Defines how frequently Dependabot checks for updates (e.g., daily, weekly). This ensures regular scanning for outdated or vulnerable dependencies.
[Not E] The milestone field is optional and used for associating pull requests with milestones. The allow field is also optional and used to specify which dependencies to update.


NEW QUESTION # 78
Which CodeQL query suite provides queries of lower severity than the default query suite?

Answer: B

Explanation:
The security-extended query suite includes additional CodeQL queries that detect lower severity issues than those in the default security-and-quality suite.
It's often used when projects want broader visibility into code hygiene and potential weak spots beyond critical vulnerabilities.
The other options listed are paths to language packs, not query suites themselves.


NEW QUESTION # 79
Which of the following statements most accurately describes push protection for secret scanning custom patterns?

Answer: D

Explanation:
Ability to detect custom patterns: Organizations can define custom patterns for detecting secrets unique to their environment. This customization ensures that push Protection can effectively identify and block even non-standard secrets [Not C].
Customizing push protection
Once push protection is enabled, you can customize it further [Not A]:
Configure push protected patterns
Customize which secret patterns are included in push protection at the enterprise or organization level.
Define custom patterns
Define custom patterns that push protection can use to identify secrets and block pushes containing these secrets Note: You can enable push protection:
At repository/organization level, if you are a repository administrator or an organization owner.
You will see alerts in the Security tab of your repository when a contributor to the repository bypasses push protection.
For your account on GitHub, as a user. This type of push protection is referred to as "push protection for users." It protects you from pushing secrets to any public repository on GitHub, but no alerts are generated.


NEW QUESTION # 80
You are configuring a CodeQL workflow for compiled languages. What happens if your workflow uses a language matrix?

Answer: A

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 # 81
......

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