GH-300 Antworten & GH-300 Prüfungsmaterialien

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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Exam Duration:100 minutes
Real Exam Qty:65
Exam Format:Multiple-choice, Scenario-based
Passing Score:700 / 1000
Exam Price:$99 USD
Available Languages:Korean, Spanish, Portuguese (Brazil), Japanese, English
Certificate Validity Period:1 year (renewable)
Related Certifications:GitHub Actions
GitHub Foundations
Sample Questions:Microsoft GH-300 Sample Questions
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-copilot

>> GH-300 Antworten <<

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Microsoft GH-300 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.
Thema 2
  • Domain 4: Prompt Crafting and Prompt Engineering This section measures skills of Software Developers and AI Interaction Designers in effectively crafting prompts to optimize Copilot’s output. It reviews foundational concepts such as prompt components, the role of language in prompting, zero-shot vs. few-shot prompting, and how chat history influences responses. Best practices and engineering principles for prompt design and training methods are also covered.
Thema 3
  • Responsible :This section of the exam measures skills of AI Ethics Officers and Risk Managers and covers the responsible and ethical usage of AI technologies. It explains the risks and limitations associated with generative AI tools, including biases in training data and the need to validate AI outputs. Candidates learn how to operate AI responsibly by identifying potential harms such as bias, fairness, privacy concerns, and mitigating these harms by applying ethical AI principles.
Thema 4
  • Privacy Fundamentals and Context Exclusions: This domain focuses on Security Engineers and Compliance Officers and addresses improving code quality with Copilot’s test suggestions and security optimizations. It covers identification of security vulnerabilities, performance enhancements, and privacy features like content exclusions at repository and organization levels with explanation of their limitations. Candidates learn about safeguarding mechanisms such as duplication detection, contractual protections, security checks, and troubleshooting guide for common Copilot issues including context exclusions and suggestion gaps.
Thema 5
  • How GitHub Copilot Works and Handles Data: Designed for Machine Learning Engineers and Data Privacy Specialists, this section covers the data lifecycle and processing behind Copilot’s code suggestions. It explains how context is gathered, prompts constructed, responses generated, and post-processed through proxy services. Candidates understand Copilot’s data policies, handling of inputs, and limitations such as context window size and data age influencing suggestion relevance.
Thema 6
  • Developer Use Cases for AI: Targeting Software Engineers and Technical Leads, this domain elaborates on how AI improves developer productivity across common tasks like learning new languages, translation, documentation, debugging, data science, and refactoring. It discusses Copilot’s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilot’s impact is also included.

Microsoft GitHub Copilot GH-300 Prüfungsfragen mit Lösungen (Q103-Q108):

103. Frage
What are the potential limitations of GitHub Copilot in maintaining existing codebases?

Antwort: D

Begründung:
"Copilot may not always understand the full context or complex dependencies in large codebases, and suggestions may require significant review." This proves that the main limitation is a lack of deep awareness of complex or large codebase contexts.
References: GitHub Copilot limitations documentation.


104. Frage
A team is using GitHub Copilot Individual in their daily development activities. They need to exclude specific files from being used to inform code completion suggestions. How can they achieve this?

Antwort: C

Begründung:
"Content exclusions are available only with GitHub Copilot Business and Enterprise. Copilot Individual users cannot configure exclusions." This confirms that the only way for the team to achieve exclusions is by upgrading to Copilot Business.
References: GitHub Copilot content exclusions documentation.


105. Frage
Which of the following statements best describes the impact of GitHub Copilot on the software development process?

Antwort: D

Begründung:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
Reference: GitHub Copilot impact documentation.


106. Frage
You use GitHub Copilot to develop a web app named App1 in a repository named Repo1.
Repo1 contains generated files and supporting artifacts.
You discover that GitHub Copilot suggestions in the IDE are being generated based on files that are NOT part of the App1 source code.
You need to ensure that GitHub Copilot ignores these files.
What should you do?

Antwort: B

Begründung:
GitHub Copilot content exclusion is the feature designed to prevent specified repository files or directories from being used as Copilot context. GitHub ' s documentation states that repository administrators can configure exclusions in the repository ' s settings under Copilot and Content exclusion. When content is excluded, it does not inform inline suggestions in other files, which directly addresses the problem of generated or supporting artifacts influencing suggestions for App1. A .gitignore file controls Git version tracking behavior, not Copilot context. Disabling inline suggestions would remove the desired feature rather than fix its context, and repository custom instructions are guidance rather than an access restriction.
Therefore, configure Copilot content exclusion rules in the GitHub Copilot settings for Repo1.


107. Frage
What is the main purpose of the duplication detection filter in GitHub Copilot?

Antwort: B


108. Frage
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