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[Hardware] GH-300 exam preparation, real Microsoft test dumps for GitHub Copilot

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【Hardware】 GH-300 exam preparation, real Microsoft test dumps for GitHub Copilot

Posted at 15 hour before      View：7 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. Free & New GH-300 dumps are available on Google Drive shared by GuideTorrent: https://drive.google.com/open?id=1wTiYCf2WI2gHjWU7b-RXpjrCfTzktxnb
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Microsoft GH-300 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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.

Microsoft GitHub Copilot Sample Questions (Q45-Q50):NEW QUESTION # 45
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?
  • A. Suggestions blocked by duplication detection filtering
  • B. Changes to content exclusion settings
  • C. Accepted chat suggestions
  • D. Code suggestions made by GitHub Copilot
Answer: B
Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.

NEW QUESTION # 46
Which of the following are true about code suggestions? (Each correct answer presents part of the solution.
Choose two.)
  • A. Code suggestions will always compile or run without modifications
  • B. Alternative code suggestions can be shown in a new tab
  • C. Code suggestions are limited to single-line suggestions
  • D. You can use keyboard shortcuts to accept the next word in a suggestion
  • E. Code suggestions are guaranteed to not expose known security vulnerabilities
Answer: B,D
Explanation:
Exact extracts:
* "You can accept individual words, lines, or entire suggestions with keyboard shortcuts." References: GitHub Copilot editor usage documentation.

NEW QUESTION # 47
Which of the following is a risk associated with using AI?
  • A. AI algorithms are incapable of perpetuating existing biases.
  • B. AI eliminates the need for data privacy regulations.
  • C. AI replaces the need for developer opportunities in most fields.
  • D. AI systems can sometimes make decisions that are difficult to interpret.
Answer: D
Explanation:
A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.

NEW QUESTION # 48
What is used by GitHub Copilot in the IDE to determine the prompt context?
  • A. The open tabs in the IDE and the current folder of the terminal.
  • B. All the code in the current repository and any git submodules.
  • C. All the code visible in the current IDE.
  • D. Information from the IDE like open tabs, cursor location, selected code.
Answer: D
Explanation:
"GitHub Copilot generates suggestions based on context from the IDE, including the file you are editing, open tabs, cursor position, and any selected code." This proves that the context is drawn from editor state information, making option A correct.
References: GitHub Copilot context and suggestions documentation.

NEW QUESTION # 49
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?
  • A. GitHub Copilot can directly convert legacy applications into cloud-native architectures.
  • B. GitHub Copilot can create and deploy full-stack applications based on a single query.
  • C. GitHub Copilot can refactor applications to align with upcoming standards.
  • D. GitHub Copilot can suggest modern programming patterns based on your code.
Answer: D
Explanation:
GitHub Copilot and GitHub Copilot Chat are powerful AI-driven tools designed to assist developers by providing context-aware code suggestions and interactive support. Specifically, in the context of modernizing applications, GitHub Copilot excels at analyzing existing code and suggesting modern programming patterns, best practices, and syntax improvements that align with contemporary development standards. For example, it can recommend updates to outdated constructs, propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.
* Why not A?GitHub Copilot does not "directly convert" legacy applications into cloud-native architectures. It can assist by suggesting code changes or patterns that support such a transition, but it doesn't autonomously perform the full conversion process, which involves architectural decisions and deployment steps beyond its scope.
* Why not C?While GitHub Copilot can generate code snippets and even larger portions of an application, it cannot create and deploy full-stack applications from a single query. It requires developer input, refinement, and integration to achieve a complete, deployable solution.
* Why not D?GitHub Copilot can assist with refactoring by suggesting improvements to existing code, but it doesn't inherently "align with upcoming standards" in a predictive sense. Its suggestions are based on current best practices and the data it was trained on, not future standards that are yet to be defined.
Thus,Bis the most accurate and realistic way GitHub Copilot aids developers in modernizing applications, leveraging its ability to provide relevant, context-based suggestions to update and improve codebases.
Reference: GitHub Copilot documentation on application modernization.

NEW QUESTION # 50
......
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