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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

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 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.

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Microsoft GitHub Advanced Security Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which of the following steps should you follow to integrate CodeQL into a third-party continuous integration system? (Each answer presents part of the solution. Choose three.)

A. Analyze code
B. Upload scan results
C. Write queries
D. Install the CLI
E. Process alerts

Answer: A,B,D

Explanation:
When integrating CodeQL outside of GitHub Actions (e.g., in Jenkins, CircleCI):
Install the CLI: Needed to run CodeQL commands.
Analyze code: Perform the CodeQL analysis on your project with the CLI.
Upload scan results: Export the results in SARIF format and use GitHub's API to upload them to your repo's security tab.
You don't need to write custom queries unless extending functionality. "Processing alerts" happens after GitHub receives the results.

NEW QUESTION # 37
When using CodeQL, what extension stores query suite definitions?

A. .ql
B. .qls
C. .qll
D. .yml

Answer: B

Explanation:
Query suite definitions in CodeQL are stored using the .qls file extension. A query suite defines a collection of queries to be run during an analysis and allows for grouping them based on categories like language, security relevance, or custom filters.
In contrast:
.ql files are individual queries.
.qll files are libraries used by .ql queries.
.yml is used for workflows, not query suites.

NEW QUESTION # 38
Which of the following secret scanning features can verify whether a secret is still active?

A. Custom patterns
B. Branch protection
C. Validity checks
D. Push protection

Answer: C

Explanation:
Validity checks, also called secret validation, allow GitHub to check if a detected secret is still active. If verified as live, the alert is marked as "valid", allowing security teams to prioritize the most critical leaks.
Push protection blocks secrets but does not check their validity. Custom patterns are user-defined and do not include live checks.

NEW QUESTION # 39
Why should you dismiss a code scanning alert?

A. If you fix the code that triggered the alert
B. If there is a production error in your code
C. If it includes an error in code that is used only for testing
D. To prevent developers from introducing new problems

Answer: C

Explanation:
You should dismiss a code scanning alert if the flagged code is not a true security concern, such as:
Code in test files
Code paths that are unreachable or safe by design
False positives from the scanner
Fixing the code would automatically resolve the alert - not dismiss it. Dismissing is for valid exceptions or noise reduction.

NEW QUESTION # 40
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 # 41
......

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