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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Exam Duration:100 minutes
Exam Format:Multiple-choice, Scenario-based
Related Certifications:GitHub Actions
GitHub Foundations
Real Exam Qty:65
Certificate Validity Period:1 year (renewable)
Exam Price:$99 USD
Passing Score:700 / 1000
Available Languages:Spanish, Portuguese (Brazil), Japanese, Korean, English
Sample Questions:Microsoft GH-300 Sample Questions
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-copilot

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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.
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 (Q44-Q49):

NEW QUESTION # 44
How can GitHub Copilot facilitate a smoother learning experience when diving into a new programming language? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
GitHub Copilot helps with learning new languages by providing guidance on common tasks and by converting comments into code, allowing developers to see syntax in action.


NEW QUESTION # 45
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: C

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 # 46
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

Answer: B,D

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.
Reference: GitHub Copilot prompt engineering best practices.


NEW QUESTION # 47
Which of the following statements correctly describes how GitHub Copilot Individual uses prompt data?
(Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
Exact extracts:
* "For GitHub Copilot Individual, prompts and code completions may be retained and used to train machine learning models to improve Copilot." References: GitHub Copilot data usage and privacy documentation.


NEW QUESTION # 48
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution. Choose two.)

Answer: A,B

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


NEW QUESTION # 49
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