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GitHub GitHub-Copilot Exam Overview:

Certification Vendor:GitHub
Exam Name:Microsoft GitHub Copilot
Exam Number:GH-300
Passing Score:700 / 1000
Real Exam Qty:60-65
Certificate Validity Period:2 years
Related Certifications:GitHub Copilot Certified Professional
Available Languages:English
Exam Price:$99 USD
Exam Format:Multiple Choice
Exam Duration:100 minutes
Sample Questions:GitHub GitHub-Copilot Sample Questions
Exam Way:Proctored (Online or Offline Test Centers)
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-copilot

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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
Topic 1
  • Developer Use Cases for AI: This section of the exam measures skills of Full-Stack Developers and Cloud Engineers and covers how AI enhances developer productivity across various tasks such as learning new programming languages, debugging, writing documentation, and refactoring code. It discusses how GitHub Copilot integrates with the Software Development Lifecycle (SDLC) and its role in modernizing legacy applications. It also highlights the use of AI for personalized responses, sample data generation, and improving overall efficiency in software development.
Topic 2
  • Responsible AI: This section of the exam measures the skills of AI Ethics Analysts and AI Developers and covers the principles of responsible AI usage, the risks associated with AI, and the limitations of generative AI tools. It includes the importance of validating AI-generated outputs and operating AI systems responsibly. It also explores potential harms such as bias, privacy concerns, and fairness issues, along with methods to mitigate these risks. The ethical considerations of AI development and deployment are also discussed.
Topic 3
  • GitHub Copilot Plans and FeaturesThis section of the exam measures the skills of Software Engineers and IT Administrators and covers different GitHub Copilot plans, including Individual, Business, and Enterprise editions. It explains the integration of GitHub Copilot within IDEs and discusses key features such as inline chat, multiple suggestions, and exception handling. The section details the policies for managing GitHub Copilot within organizations, including auditing logs and API management. It also highlights advanced functionalities like knowledge bases for improved code quality and best practices for Copilot Chat usage.
Topic 4
  • Privacy Fundamentals and Context Exclusions: This section of the exam measures skills of Cybersecurity Specialists and Compliance Officers and covers privacy safeguards and content exclusion settings in GitHub Copilot. It explains how Copilot can identify security vulnerabilities, suggest optimizations, and enforce secure coding practices. It also includes details on content ownership, data filtering mechanisms, and exclusion configurations. The section concludes with troubleshooting guidelines for managing context exclusions and ensuring compliance with organizational security policies.
Topic 5
  • Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.
Topic 6
  • Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.

GitHub CopilotCertification Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
Select a strategy to increase the performance of GitHub Copilot Chat.

Answer: C

Explanation:
Applying prompt engineering techniques to be more specific is the best strategy to increase the performance and relevance of GitHub Copilot Chat's responses.


NEW QUESTION # 36
Which principle emphasizes that AI systems should be understandable and provide clear information on how they work?

Answer: B

Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide clear information about their operations. This ensures that users can understand how the AI arrivesat its decisions and suggestions.


NEW QUESTION # 37
How does GitHub Copilot typically handle code suggestions that involve deprecated features or syntax of programming languages?

Answer: A

Explanation:
GitHub Copilot generates suggestions from patterns in its training corpus, so if deprecated syntax or features were part of that data, it can still propose them-developers must review and update any outdated code.


NEW QUESTION # 38
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution.
Choose two.)

Answer: A,C

Explanation:
GitHub Copilot Individual is billed as a monthly or annual subscription.


NEW QUESTION # 39
How does GitHub Copilot Chat help to fix security issues in your codebase?

Answer: C

Explanation:
Copilot Chat can analyze your code, highlight known vulnerability patterns, and annotate its suggestions with explanations of the security issues and proposed fixes.


NEW QUESTION # 40
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