Microsoft GH-500유효한시험덤프 - GH-500최신덤프문제

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Microsoft GH-500 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Advanced Security
Exam Number:GH-500
Related Certifications:GitHub Advanced Security Certification
Exam Duration:120 minutes
Certificate Validity Period:1 year
Exam Price:$165 USD
Passing Score:700 out of 1000
Available Languages:Korean, Japanese, Chinese (Simplified), French, English, German, Spanish
Real Exam Qty:40-60
Exam Format:Case study, Drag and drop, Multiple choice
Sample Questions:Microsoft GH-500 Sample Questions
Exam Way:Online (Proctored) or In-person at a testing center
Pre Condition:No mandatory prerequisites. Recommended to have experience with GitHub and basic understanding of security concepts.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-advanced-security/

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GH-500유효한 시험덤프 100％시험패스 인증덤프

IT업계에 종사하고 계신 분은Microsoft GH-500 시험을 패스하여 자격증을 취득하려고 검색하다 저희 블로그를 보게 되시고 저희 사이트까지 방문하게 될것입니다. 방문하는 순간 Microsoft GH-500시험에 대한 두려움이 사라질것입니다. 완벽한 구매후 서비스까지 겸비하고 있어 자격증을 취득하는데서의 믿음직스러운 동반자로 되어드릴게요.

Microsoft GH-500 시험요강:

주제소개
주제 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.
주제 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.
주제 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.
주제 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.
주제 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.

최신 GitHub Administrator GH-500 무료샘플문제 (Q13-Q18):

질문 # 13
Which of the following formats are used to describe a code scanning alert from CodeQL?

정답：A

설명：
CodeQL security queries are associated with Common Weakness Enumeration (CWE) classifications. CWE provides standardized categories for classes of software weaknesses, such as improper input validation, path traversal, or SQL injection. GitHub CodeQL query metadata and alert tags can contain CWE identifiers, and GitHub allows code scanning alerts to be filtered by CWE tags. CVEs identify specific publicly disclosed vulnerabilities rather than general weakness categories in source-code analysis. A GHSA identifies a GitHub Security Advisory, while VEX communicates whether a product is affected by a known vulnerability. For CodeQL findings, CWE is therefore the relevant classification system used to describe the type of weakness represented by an alert. GitHub's code scanning API also exposes CodeQL rules containing external/cwe/...
tags.


질문 # 14
When using code scanning and GitHub Actions on Windows, what is the relative difference between the minute consumption of code scanning jobs and jobs on Linux runners?

정답：B

설명：
Under the GitHub Actions minute-multiplier model tested by this question, jobs executed on GitHub-hosted Windows runners consume included minutes at twice the rate of equivalent Linux-runner jobs. Linux has a multiplier of 1, while Windows has a multiplier of 2; historically macOS used a substantially higher multiplier. Because CodeQL advanced setup can execute as a GitHub Actions workflow, the operating system selected with runs-on affects Actions consumption in the same way as other GitHub-hosted workflows.
GitHub's newer billing documentation increasingly expresses runner usage through operating-system-specific per-minute pricing rather than only multiplier terminology, but the exam question specifically tests the established Windows-versus-Linux minute multiplier. Therefore, the expected answer is 2x higher.


질문 # 15
What scenario demonstrates the use of Dependabot security updates?

정답：C

설명：
Dependabot security updates are automated pull requests generated by the GitHub tool to update project dependencies with known security vulnerabilities, such as those listed in the GitHub Advisory Database. This feature helps developers automatically patch security risks in their codebase by creating pull requests that update dependencies to the minimum secure version without breaking the dependency graph.
How Dependabot Security Updates Work
1. Vulnerability Detection: Dependabot scans your repository's dependencies and checks them against the GitHub Advisory Database.
2. Alerts: If a vulnerable dependency is detected, Dependabot sends an alert to the user.
3. Automated Pull Request: For repositories where security updates are enabled, Dependabot automatically creates a pull request to fix the vulnerability.
4. Update to Secure Version: The pull request updates the vulnerable dependency to the minimum version that contains a patch for the known security issue.
5. Resolution: The pull request provides details, including release notes and commits, and is linked to the security alert for easy review and merging.


질문 # 16
Which of the following is the best way to prevent developers from adding secrets to the repository?

정답：D

설명：
The best proactive control is push protection. It scans for secrets during a git push and blocks the commit before it enters the repository.
Other options (like CODEOWNERS or security managers) help with oversight but do not prevent secret leaks.
Making a repo public would increase the risk, not reduce it.


질문 # 17
After defining a secret scanning custom pattern, what is the final step before publishing the pattern?

정답：B

설명：
Defining a custom pattern for a repository [See step 6 below]
Before defining a custom pattern, you must ensure that Secret Protection is enabled on your 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 "Security" section of the sidebar, click Advanced Security.
4. Under "Secret Protection", to the right of "Custom patterns", click New pattern.
5. Enter the details for your new custom pattern. You must at least provide the name for your pattern, and a regular expression for the format of your secret pattern.
*-> 6. When you're ready to test your new custom pattern, to identify matches in the repository without creating alerts, click Save and dry run.
7. When the dry run finishes, you'll see a sample of results (up to 1000). Review the results and identify any false positive results.

8. Edit the new custom pattern to fix any problems with the results, then, to test your changes, click Save and dry run.
9. When you're satisfied with your new custom pattern, click Publish pattern.
10. Optionally, to enable push protection for your custom pattern, click Enable.


질문 # 18
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