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GH-300 Online Exam, GH-300 Latest Mock Test

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GH-300 Online Exam, GH-300 Latest Mock Test

Posted at yesterday 18:45      View：4 | Replies：0        Print      Only Author   [Copy Link] 1#
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Microsoft GH-300 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.
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.

Microsoft GitHub Copilot Sample Questions (Q99-Q104):NEW QUESTION # 99
How can GitHub Copilot assist with code refactoring tasks?
  • A. GitHub Copilot can remove unnecessary files from the project directory.
  • B. GitHub Copilot can suggest refactoring improvements for better code quality.
  • C. GitHub Copilot can fix syntax errors without user input.
  • D. GitHub Copilot can automatically rewrite code to follow best practices.
Answer: B
Explanation:
GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.
Reference: GitHub Copilot documentation on code refactoring assistance.

NEW QUESTION # 100
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?
  • A. Code suggestions made by GitHub Copilot
  • B. Accepted chat suggestions
  • C. Changes to content exclusion settings
  • D. Suggestions blocked by duplication detection filtering
Answer: C
Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.

NEW QUESTION # 101
How can GitHub Copilot aid developers in writing documentation for their code?
  • A. GitHub Copilot can only generate content in markdown format.
  • B. GitHub Copilot can suggest summaries or descriptions based on the code's functionality.
  • C. GitHub Copilot can automatically generate complete and detailed documentation.
  • D. GitHub Copilot cannot assist in writing documentation or comments.
Answer: B
Explanation:
"Copilot can suggest comments and documentation summaries that describe the functionality of the code being written." This makes option C correct, as Copilot provides summaries or descriptions rather than full automatic documentation.
References: GitHub Copilot documentation features.

NEW QUESTION # 102
Which of the following does GitHub Copilot's LLM derive context from when producing a response?
  • A. Frequency of commits to the repository
  • B. Neighboring or related files within a project
  • C. Syntax highlighting scheme of the code in the IDE
  • D. Version control system integrated with the IDE
Answer: B
Explanation:
"Copilot may use context from neighboring or related files in the project to improve the accuracy of its suggestions." This confirms that context is enriched with information from related files, making option C correct.
References: GitHub Copilot context derivation documentation.

NEW QUESTION # 103
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?
  • A. Reviewing the user's browsing history to identify public repositories
  • B. Running code suggestions through filters designed to detect public code
  • C. Analyzing the context and structure of the code being written
  • D. Comparing suggestions against public code using machine learning.
Answer: B
Explanation:
When the public code filter is enabled, GitHub Copilot runs code suggestions through filters designed to detect matches with publicly available code. This helps prevent the generation of code that might infringe on copyright or licensing agreements.
Reference: GitHub Copilot documentation on public code filtering and licensing.

NEW QUESTION # 104
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