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Microsoft GH-300 Exam Tips & GH-300 Reliable Exam Blueprint
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The Microsoft GH-300 certification provides is beneficial to accelerate your career in the tech sector. Today, the Microsoft certification is a fantastic choice to get high-paying jobs and promotions, and to achieve it, you must crack the challenging GH-300 Exam. It is critical to prepare with actual GitHub Copilot (GH-300) exam questions if you have less time and want to clear the test in a short time.
Microsoft GH-300 Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 2
- 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 3
- 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.
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.
Topic 5
- 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.
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Microsoft GitHub Copilot Sample Questions (Q51-Q56):
NEW QUESTION # 51
When using GitHub Copilot to identify missing tests in your codebase, which of the following is the most important factor to consider?
- A. Close all the tabs in your IDE that do not have tests in them.
- B. Using well-known coding practices in your repository.
- C. Ensuring that the correct context is available to GitHub Copilot.
- D. Having a high test coverage percentage in the codebase.
Answer: C
Explanation:
"The accuracy of Copilot's test generation depends heavily on the available context, including existing code, file structure, and open selections." This confirms that providing the correct context is the most important factor.
References: GitHub Copilot testing documentation.
NEW QUESTION # 52
How can the concept of fairness be integrated into the process of operating an AI tool?
- A. Regularly monitoring the AI tool's performance will ensure fairness in its outputs.
- B. Focusing on collecting large datasets for training will ensure fairness.
- C. Training AI data and algorithms to be free from biases will ensure fairness.
- D. Focusing on accessibility will ensure fairness.
Answer: C
Explanation:
Fairness in AI tools is achieved by training the data and algorithms to be free from biases. This ensures that the tool treats all users equitably and avoids discriminatory outcomes.
NEW QUESTION # 53
What is a limitation of content exclusions?
- A. Content exclusions are only available in the GitHub Copilot Individual plan.
- B. Repository administrators and organization owners cannot manage content exclusion settings.
- C. Content exclusions can only be configured by an enterprise administrator.
- D. Content exclusions can be worked around as it is only available for Git repositories.
Answer: C
Explanation:
"Content exclusions can only be configured at the enterprise level by an enterprise administrator. Repository or organization admins cannot configure them directly." This confirms that configuration is limited to enterprise administrators, making option C correct.
References: GitHub Copilot content exclusions configuration documentation.
NEW QUESTION # 54
Why is code reviewing still necessary when using GitHub Copilot to write tests?
- A. Because GitHub Copilot can cover all possible scenarios in your test cases.
- B. Because GitHub Copilot generates the best code possible for the test scenario.
- C. Because GitHub Copilot's generated test cases may not cover all possible scenarios.
- D. Because GitHub Copilot replaces the need for manual testing.
Answer: C
Explanation:
Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.
Reference: GitHub Copilot testing best practices.
NEW QUESTION # 55
How can GitHub Copilot assist with code refactoring tasks?
- A. GitHub Copilot can suggest refactoring improvements for better code quality.
- B. GitHub Copilot can fix syntax errors without user input.
- C. GitHub Copilot can remove unnecessary files from the project directory.
- D. GitHub Copilot can automatically rewrite code to follow best practices.
Answer: A
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 # 56
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