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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:GitHub Copilot Exam (GH-300)
Exam Number:GH-300
Certificate Validity Period:2 years
Exam Price:USD 99
Available Languages:Spanish, English, Japanese, Portuguese (Brazil), Korean
Passing Score:700/1000
Exam Format:Multiple choice, Drag and drop, Multiple response, Scenario-based questions
Related Certifications:GitHub Copilot Fundamentals Part 2
GitHub Copilot Fundamentals Part 1
Real Exam Qty:60-75 (approximately 65 scored + unscored items)
Exam Duration:100-120
Recommended Training:GitHub Copilot Fundamentals Part 1
GitHub Copilot Fundamentals Part 2
Exam Registration:Microsoft Learn Certification Page
Pearson VUE Scheduling
Sample Questions:Microsoft GH-300 Sample Questions
Exam Way:Online proctored (Pearson VUE) or test center
Pre Condition:Basic GitHub knowledge and experience using at least one programming language; familiarity with GitHub Copilot recommended but not strictly required
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
  • 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 2
  • 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 3
  • 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 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
  • 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 6
  • 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.

Microsoft GitHub Copilot Sample Questions (Q14-Q19):

NEW QUESTION # 14
Select a strategy to increase the performance of GitHub Copilot Chat.

Answer: C

Explanation:
Applying prompt engineering techniques to be more specific is the best strategy to increase the performance and relevance of GitHub Copilot Chat's responses.


NEW QUESTION # 15
What role does chat history play in GitHub Copilot's code suggestions?

Answer: C

Explanation:
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.
Reference: GitHub Copilot Chat history documentation.


NEW QUESTION # 16
1.
blog.yatricloud.com
blog.yatricloud.com

Answer: A,D

Explanation:
The GitHub Copilot usage metrics API provides insights into the acceptance rate of code suggestions and Copilot Chat specific suggestions acceptance metrics, helping organizations evaluate its effectiveness.
Reference: GitHub Copilot usage metrics API documentation.


NEW QUESTION # 17
When can GitHub Copilot still use content that was excluded using content exclusion?

Answer: C

Explanation:
GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.


NEW QUESTION # 18
Which GitHub Copilot plan could an Azure DevOps organization use without requiring a GitHub Enterprise license?

Answer: D

Explanation:
"GitHub Copilot for Azure DevOps is available to Azure DevOps organizations without requiring a GitHub Enterprise license." This confirms that Copilot for Azure DevOps is the correct plan for such organizations.
References: GitHub Copilot for Azure DevOps documentation.


NEW QUESTION # 19
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