Verified Microsoft GH-300: Online GitHub Copilot Bootcamps - Professional Itcertkey GH-300 Exam Topics

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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:GitHub Copilot Exam (GH-300)
Exam Number:GH-300
Passing Score:700/1000
Available Languages:Spanish, Portuguese (Brazil), Korean, English, Japanese
Exam Duration:100-120
Exam Price:USD 99
Real Exam Qty:60-75 (approximately 65 scored + unscored items)
Certificate Validity Period:2 years
Related Certifications:GitHub Copilot Fundamentals Part 2
GitHub Copilot Fundamentals Part 1
Exam Format:Multiple choice, Scenario-based questions, Drag and drop, Multiple response
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/

>> Online GH-300 Bootcamps <<

Desktop-Based/Online Microsoft GH-300 Practice Test

New developments in the tech sector always bring new job opportunities. These new jobs have to be filled with the GitHub Copilot (GH-300) certification holders. So to fill the space, you need to pass the Microsoft GH-300 exam. Earning the GitHub Copilot (GH-300) certification helps you clear the obstacles you face while working in the Microsoft field. To get prepared for the GitHub Copilot (GH-300) certification exam, applicants face a lot of trouble if the study material is not updated.

Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.
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
  • 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.

Microsoft GitHub Copilot Sample Questions (Q69-Q74):

NEW QUESTION # 69
When using GitHub Copilot to identify missing tests in your codebase, which of the following is the most important factor to consider?

Answer: C

Explanation:
"The accuracy of Copilot's test generation depends heavily on the available context, including existing code, file structure, and open selections." This confirms that providing the correct context is the most important factor.
References: GitHub Copilot testing documentation.


NEW QUESTION # 70
Which GitHub Copilot plan allows for prompt and suggestion collection?

Answer: D

Explanation:
GitHub Copilot Enterprise allows for prompt and suggestion collection, enabling organizations to analyze and improve their usage of the tool.
Reference: GitHub Copilot Enterprise data collection documentation.


NEW QUESTION # 71
How can GitHub Copilot aid developers in writing documentation for their code?

Answer: C

Explanation:
"Copilot can suggest comments and documentation summaries that describe the functionality of the code being written." This makes option C correct, as Copilot provides summaries or descriptions rather than full automatic documentation.
References: GitHub Copilot documentation features.


NEW QUESTION # 72
A social media manager wants to use AI to filter content. How can they promote transparency in the platform' s AI operations?

Answer: D

Explanation:
Exact extracts:
* "Transparency. AI systems should be understandable."
References: Microsoft Responsible AI guidelines and transparency notes.


NEW QUESTION # 73
What is the best way to share feedback about GitHub Copilot Chat when using it on GitHub Mobile?

Answer: D

Explanation:
"On GitHub Mobile, you can use emojis in the Copilot Chat interface to provide quick feedback on the responses." This makes option A correct.
References: GitHub Copilot Mobile documentation.


NEW QUESTION # 74
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GH-300 Exam Topics: https://www.itcertkey.com/GH-300_braindumps.html

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