Microsoft GH-500 Test Prep - Review GH-500 Guide

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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
  • 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 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 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 (Q63-Q68):

NEW QUESTION # 63
When does Dependabot alert you of a vulnerability in your software development process?

Answer: B

Explanation:
Dependabot alerts are generated as soon as GitHub detects a known vulnerability in one of your dependencies. GitHub does this by analyzing your repository's dependency graph and matching it against vulnerabilities listed in the GitHub Advisory Database. Once a match is found, the system raises an alert automatically without waiting for a PR or manual action.
This allows organizations to proactively mitigate vulnerabilities as early as possible, based on real-time detection.


NEW QUESTION # 64
Which of the following Watch settings could you use to get Dependabot alert notifications? Each answer presents part of the solution. (Choose two.)

Answer: B,C

Explanation:
To receive Dependabot alert notifications for a repository, you can utilize the following Watch settings:
Custom setting: Allows you to tailor your notifications, enabling you to subscribe specifically to security alerts, including those from Dependabot.
All Activity setting: Subscribes you to all notifications for the repository, encompassing issues, pull requests, and security alerts like those from Dependabot.
The Participating and @mentions setting limits notifications to conversations you're directly involved in or mentioned, which may not include security alerts. The Ignore setting unsubscribes you from all notifications, including critical security alerts.


NEW QUESTION # 65
A colleague ignores a code scanning alert. What are the implications of the colleague's action?
Each answer presents part of the solution. (Choose three.)

Answer: B,C,E

Explanation:
If you configure code scanning using CodeQL, you can also find data-flow problems in your code.
Data-flow analysis finds potential security issues in code, such as: using data insecurely[C], passing dangerous arguments to functions [D], and leaking sensitive information[B].
When code scanning reports data-flow alerts, GitHub shows you how data moves through the code. Code scanning allows you to identify the areas of your code that leak sensitive information, and that could be the entry point for attacks by malicious users.


NEW QUESTION # 66
In a private repository, what minimum requirements does GitHub need to generate a dependency graph? (Each answer presents part of the solution. Choose two.)

Answer: B,D

Explanation:
Comprehensive and Detailed Explanation:
To generate a dependency graph for a private repository, GitHub requires:
Dependency graph enabled: The repository must have the dependency graph feature enabled. This can be configured at the organization level to apply to all new private repositories.
Access to manifest and lock files: GitHub needs read-only access to the repository's dependency manifest and lock files (e.g., package.json, requirements.txt) to identify and map dependencies.


NEW QUESTION # 67
What is the first step in CodeQL analysis?

Answer: D

Explanation:
Before CodeQL can execute security queries, the source code must first be represented in a CodeQL database.
Database creation extracts structured, relational information from the source code and, where applicable, observes or performs the build process required for the language being analyzed. Once the database exists, CodeQL can execute queries against it. The resulting query output is then interpreted using query metadata and can ultimately be converted to a format such as SARIF for presentation as code scanning alerts.
Therefore, running queries, interpreting results, and converting results are later phases of analysis. GitHub's CodeQL documentation explicitly identifies creation of the CodeQL database as a prerequisite for analyzing source code with CodeQL queries, making preparation/database creation the correct first step.


NEW QUESTION # 68
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