UPDATED GITHUB-COPILOT RELIABLE EXAM QUESTION - PASS GITHUB-COPILOT EXAM

Updated GitHub-Copilot Reliable Exam Question - Pass GitHub-Copilot Exam

Updated GitHub-Copilot Reliable Exam Question - Pass GitHub-Copilot Exam

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Tags: GitHub-Copilot Reliable Exam Question, Valid GitHub-Copilot Exam Online, Test GitHub-Copilot Questions Pdf, New GitHub-Copilot Test Bootcamp, Fresh GitHub-Copilot Dumps

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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
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
  • How GitHub Copilot Works and Handles DataThis section of the exam measures the skills of Data Security Specialists and DevOps Engineers and covers how GitHub Copilot processes data, handles code suggestions and manages privacy concerns. It explains the data pipeline for Copilot’s suggestions, how it gathers context, and how prompts are processed through its AI model. The section also discusses the limitations of AI-generated code, the effects of historical data on suggestions, and the role of prompt crafting. Best practices for improving prompt effectiveness and optimizing AI-generated responses are included.

GitHub CopilotCertification Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

  • A. The Gen AI tool is overloaded with too many requests at once.
  • B. The training data might contain biases or inconsistencies.
  • C. The Gen AI tool is experiencing downtime and is not fully recovered.
  • D. The Gen AI tool is programmed with a focus on creativity over factual accuracy.

Answer: B

Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.


NEW QUESTION # 23
Which of the following prompts can be used to guide GitHub Copilot Chat in refactoring code for quality improvements? (Each correct answer presents part of the solution. Choose two.)

  • A. "Predict future coding trends and update my codebase accordingly."
  • B. "Show me how to improve the readability of this function."
  • C. "Refactor my application to meet the latest coding standards."
  • D. "Suggest ways to enhance the maintainability of this code segment."

Answer: B,D

Explanation:
Effective prompts for refactoring include requests that focus on specific quality improvements, such as readability and maintainability. These prompts guide GitHub Copilot to provide relevant and actionable suggestions.


NEW QUESTION # 24
Select a strategy to increase the performance of GitHub Copilot Chat.

  • A. Use a single GitHub Copilot Chat query to find resolutions for the collection of technical requirements
  • B. Apply prompt engineering techniques to be more specific
  • C. Optimize the usage of memory-intensive operations within generated code
  • D. Limit the number of concurrent users accessing GitHub Copilot Chat

Answer: B

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 # 25
How can GitHub Copilot assist developers during the requirements analysis phase of the Software Development Life Cycle (SDLC)?

  • A. By providing templates and code snippets that help in documenting requirements.
  • B. By managing stakeholder communication and meetings.
  • C. By identifying and fixing potential requirement conflicts when using /help.
  • D. By automatically generating detailed requirements documents.

Answer: A

Explanation:
GitHub Copilot can assist during the requirements analysis phase by providing templates and code snippets that aid in documenting requirements. This helps streamline the process of capturing and organizing project requirements.


NEW QUESTION # 26
How does GitHub Copilot Chat help in understanding the existing codebase?

  • A. By answering questions about the code and generating explanations.
  • B. By automatically refactoring code to improve readability.
  • C. By providing visual diagrams of the code structure.
  • D. By running code linters and formatters.

Answer: A

Explanation:
GitHub Copilot Chat helps in understanding existing codebases by answering questions about the code and generating explanations. This allows developers to quickly grasp the functionality and structure of unfamiliar code.


NEW QUESTION # 27
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