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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
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 Copilotโ€™s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilotโ€™s impact is also included.
Topic 2
  • Domain 6: Testing with GitHub Copilot: This section measures abilities of QA Engineers and Test Automation Specialists to use Copilot for test generation, including unit and integration tests. It explains how Copilot can identify edge cases and assist in writing assertions. The domain also covers different Copilot subscription SKUs, privacy considerations, organizational code suggestion settings, and configuration files related to Copilot.
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
  • Privacy Fundamentals and Context Exclusions: This domain focuses on Security Engineers and Compliance Officers and addresses improving code quality with Copilotโ€™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.

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Microsoft GitHub Copilot Sample Questions (Q50-Q55):

NEW QUESTION # 50
Why is code reviewing still necessary when using GitHub Copilot to write tests?

Answer: B

Explanation:
Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.


NEW QUESTION # 51
How can the concept of fairness be integrated into the process of operating an AI tool?

Answer: D

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 # 52
Which of the following statements best describes the impact of GitHub Copilot on the software development process?

Answer: C

Explanation:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
Reference: GitHub Copilot impact documentation.


NEW QUESTION # 53
What are the additional checks that need to pass before the GitHub Copilot responses are submitted to the user? (Each correct answer presents part of the solution. Choose two.)

Answer: A,C

Explanation:
Exact extracts:
* "Copilot applies post-processing checks for code quality to ensure that responses meet baseline standards." References: GitHub Copilot safeguards documentation.


NEW QUESTION # 54
You have a GitHub Copilot subscription.
You need to start an interactive GitHub Copilot CLI session from the root folder of a project.
If you are NOT authenticated, GitHub Copilot must prompt you to sign in.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 55
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