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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft
Exam Name:GitHub Copilot
Exam Number:GH-300
Related Certifications:GitHub Foundations
GitHub Actions
Certificate Validity Period:1 year (renewable)
Exam Format:Multiple-choice, Scenario-based
Real Exam Qty:65
Passing Score:700 / 1000
Exam Duration:100 minutes
Exam Price:$99 USD
Available Languages:Portuguese (Brazil), Korean, Spanish, English, Japanese
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
  • 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 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
  • 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.

Microsoft GitHub Copilot Sample Questions (Q104-Q109):

NEW QUESTION # 104
What are the different ways to give context to GitHub Copilot to get more precise responses? (Each correct answer presents part of the solution. Choose two.)

Answer: A,D

Explanation:
Exact extracts:
* "You can give Copilot Chat additional context using variables such as @workspace or @file to scope the conversation." References: GitHub Copilot Chat variables and context documentation.


NEW QUESTION # 105
How does GitHub Copilot typically handle code suggestions that involve deprecated features or syntax of programming languages?

Answer: A

Explanation:
"GitHub Copilot may sometimes suggest deprecated code, APIs, or patterns if these appear in its training data. Users are responsible for reviewing and updating the suggestions." This confirms that Copilot does not automatically update or reject deprecated features, but may still suggest them if they were part of training.
References: GitHub Copilot usage limitations documentation.


NEW QUESTION # 106
What reasons could apply if code suggestions are not working in your editor? (Select three.)

Answer: A,C,E

Explanation:
Exact extracts:
* "Copilot requires an active internet connection to provide suggestions."
* "Copilot does not support all programming languages."
References: GitHub Copilot troubleshooting documentation.


NEW QUESTION # 107
How does the /tests slash command assist developers?

Answer: D

Explanation:
The /tests slash command in GitHub Copilot Chat creates unit tests for the selected code, helping developers ensure the functionality and reliability of their code.


NEW QUESTION # 108
How does GitHub Copilot Chat utilize its training data and external sources to generate responses when answering coding questions?

Answer: B

Explanation:
GitHub Copilot Chat combines its training data, code from user repositories, and external sources like Bing to generate comprehensive and relevant responses to coding questions.


NEW QUESTION # 109
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