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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 4
  • 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 5
  • 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.

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Microsoft GitHub Copilot Sample Questions (Q15-Q20):

NEW QUESTION # 15
What is a key consideration when relying on GitHub Copilot Chat's explanations of code functionality and proposed improvements?

Answer: A

Explanation:
While GitHub Copilot Chat can provide helpful explanations and suggestions, it's crucial to review and validate the generated output. Copilot's suggestions are based on its training data, and they may not always be perfectly accurate or complete. Human judgment is essential to ensure the quality and correctness of the code.


NEW QUESTION # 16
How does GitHub Copilot Chat help in understanding the existing codebase?

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 # 17
What is zero-shot prompting?

Answer: E

Explanation:
"Zero-shot prompting means asking the model to perform a task without providing examples-only the question or task description is given." This confirms that the correct definition of zero-shot prompting is providing no examples, only a direct prompt.
References: GitHub Copilot and AI prompting documentation.


NEW QUESTION # 18
You use GitHub Copilot Chat to debug a function.
You discover that GitHub Copilot is using irrelevant chat threads to generate output.
You need to ensure that GitHub Copilot only uses new input to generate output.
What should you do, and how will this affect the prompt process flow?

Answer: B

Explanation:
GitHub Copilot Chat uses conversation history as part of the context for later responses. GitHub ' s prompt- engineering guidance recommends keeping chat history relevant, while GitHub ' s AI-usage guidance specifically advises starting a new conversation when moving to unrelated work because a long thread carries prior history into subsequent requests. In this scenario, irrelevant earlier chat threads are contaminating the context used for debugging. Starting a new Copilot Chat session creates a clean conversational context for the new prompt, so the previous thread is no longer included as model input for that new conversation.
Continuing in the same chat would preserve the unwanted context. Therefore, the correct action is to start a new chat, with the effect of removing the prior chat history from the new model input.


NEW QUESTION # 19
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
GitHub Copilot Individual is billed as a monthly or annual subscription.
Reference: GitHub Copilot Individual pricing.


NEW QUESTION # 20
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