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[General] Valid Test GH-300 Passing Score, Ensure to pass the GH-300 Exam

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【General】 Valid Test GH-300 Passing Score, Ensure to pass the GH-300 Exam

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The candidates can test themselves for the GitHub Copilot exam day by attempting the GitHub Copilot GH-300 practice test on the software. There is preparation material available on the GH-300 Practice Exam software by Free4Torrent to study for the Microsoft GH-300 test.
Microsoft GH-300 Exam Syllabus Topics:
TopicDetails
Topic 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.

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Microsoft GitHub Copilot Sample Questions (Q16-Q21):NEW QUESTION # 16
How does GitHub Copilot Chat help to fix security issues in your codebase?
  • A. By automatically refactoring the entire codebase to remove vulnerabilities.
  • B. By annotating the given suggestions with known vulnerability patterns.
  • C. By providing detailed reports on the security vulnerabilities present in the codebase.
  • D. By enforcing strict coding standards that prevent the introduction of vulnerabilities.
Answer: B
Explanation:
"When Copilot Chat generates suggestions, it may annotate them with warnings about known insecure patterns, helping developers identify potential vulnerabilities in context." This indicates that Copilot Chat assists with security by annotating code suggestions with vulnerability patterns.
References: GitHub Copilot Chat security and responsible use documentation.

NEW QUESTION # 17
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)
  • A. By running the gh copilot fix command.
  • B. By highlighting the code you want to fix, right-clicking, and selecting "Fix using GitHub Copilot."
  • C. By using the "/fix" command in GitHub Copilot in-line chat.
  • D. By adding comments to your code and triggering a suggestion.
  • E. By highlighting the code you want to fix, right-clicking, and selecting "Refactor using GitHub Copilot."
Answer: D,E
Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting "Refactor using GitHub Copilot" from the context menu.
Reference: GitHub Copilot refactoring documentation.

NEW QUESTION # 18
What types of prompts or code snippets might be flagged by the GitHub Copilot toxicity filter? (Each correct answer presents part of the solution. Choose two.)
  • A. Sexually suggestive or explicit content
  • B. Code that contains logical errors or produces unexpected results
  • C. Code comments containing strong opinions or criticisms
  • D. Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)
Answer: A,D
Explanation:
GitHub Copilot includes a toxicity filter to prevent the generation of harmful or inappropriate content. This filter flags prompts or code snippets that contain hate speech, discriminatory language, or sexually suggestive or explicit content. This ensures a safe and respectful coding environment.

NEW QUESTION # 19
Why might a Generative AI (Gen AI) tool create inaccurate outputs?
  • A. The Gen AI tool is experiencing downtime and is not fully recovered.
  • B. The training data might contain biases or inconsistencies.
  • C. The Gen AI tool is programmed with a focus on creativity over factual accuracy.
  • D. The Gen AI tool is overloaded with too many requests at once.
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 # 20
How does GitHub Copilot Chat help in understanding the existing codebase?
  • A. By automatically refactoring code to improve readability.
  • B. By running code linters and formatters.
  • C. By providing visual diagrams of the code structure.
  • D. By answering questions about the code and generating explanations.
Answer: D
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 # 21
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