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Microsoft GH-300 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	GitHub Copilot
Exam Number:	GH-300
Passing Score:	700 / 1000
Exam Price:	$99 USD
Available Languages:	Korean, Japanese, Portuguese (Brazil), English, German, Chinese (Simplified), French, Arabic (Saudi Arabia), Spanish
Certificate Validity Period:	1 year
Real Exam Qty:	60–65
Exam Format:	Scenario-based, Proctored, Multiple-choice
Exam Duration:	100 minutes
Recommended Training:	Microsoft Learn: GitHub Copilot Learning Path
Exam Registration:	Pearson VUE Registration Microsoft Learn Exam Details
Sample Questions:	Microsoft GH-300 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE test centers
Pre Condition:	Basic familiarity with GitHub, software development workflows, and at least one programming language; no formal prerequisites required
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/GH-300

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Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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 6	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.

Microsoft GitHub Copilot Sample Questions (Q53-Q58):

NEW QUESTION # 53
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?

A. GitHub Copilot can suggest modern programming patterns based on your code.
B. GitHub Copilot can create and deploy full-stack applications based on a single query.
C. GitHub Copilot can refactor applications to align with upcoming standards.
D. GitHub Copilot can directly convert legacy applications into cloud-native architectures.

Answer: A

Explanation:
GitHub Copilot and GitHub Copilot Chat are powerful AI-driven tools designed to assist developers by providing context-aware code suggestions and interactive support. Specifically, in the context of modernizing applications, GitHub Copilot excels at analyzing existing code and suggesting modern programming patterns, best practices, and syntax improvements that align with contemporary development standards. For example, it can recommend updates to outdated constructs, propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.
Why not A? GitHub Copilot does not "directly convert" legacy applications into cloud-native architectures. It can assist by suggesting code changes or patterns that support such a transition, but it doesn't autonomously perform the full conversion process, which involves architectural decisions and deployment steps beyond its scope.
Why not C? While GitHub Copilot can generate code snippets and even larger portions of an application, it cannot create and deploy full-stack applications from a single query. It requires developer input, refinement, and integration to achieve a complete, deployable solution.
Why not D? GitHub Copilot can assist with refactoring by suggesting improvements to existing code, but it doesn't inherently "align with upcoming standards" in a predictive sense. Its suggestions are based on current best practices and the data it was trained on, not future standards that are yet to be defined.
Thus, B is the most accurate and realistic way GitHub Copilot aids developers in modernizing applications, leveraging its ability to provide relevant, context-based suggestions to update and improve codebases.

NEW QUESTION # 54
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)

A. By adding comments to your code and triggering a suggestion.
B. By running the gh copilot fix command.
C. By highlighting the code you want to fix, right-clicking, and selecting " Fix using GitHub Copilot. "
D. By highlighting the code you want to fix, right-clicking, and selecting " Refactor using GitHub Copilot.
"
E. By using the " /fix " command in GitHub Copilot in-line chat.

Answer: A,D

Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting " Refactor using GitHub Copilot " from the context menu.
Reference: GitHub Copilot refactoring documentation.

NEW QUESTION # 55
What is zero-shot prompting?

A. Only giving GitHub Copilot a question as a prompt and no examples
B. Giving as little context to GitHub Copilot as possible
C. Telling GitHub Copilot it needs to show only the correct answer
D. Giving GitHub Copilot examples of the algorithm and outcome you want to use
E. Giving GitHub Copilot examples of the problem you want to solve

Answer: A

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 # 56
What are the effects of content exclusions? (Each correct answer presents part of the solution. Choose two.)

A. The excluded content is no longer used while debugging the code.
B. The IDE will not count coding suggestions in the excluded content.
C. The excluded content is not directly available to GitHub Copilot to use as context.
D. GitHub Copilot suggestions are no longer available in the excluded files.

Answer: C,D

Explanation:
Content exclusions prevent GitHub Copilot from using the excluded content as context and stop suggestions from being generated in those files.

NEW QUESTION # 57
Which of the following is correct about GitHub Copilot Knowledge Bases?

A. All file types are indexed
B. It is an Enterprise feature
C. All repos are indexed
D. Indexing is static

Answer: B

Explanation:
GitHub Copilot Knowledge Bases is an Enterprise feature that allows the tool to use an organization's internal documentation and code for more accurate and relevant suggestions.

NEW QUESTION # 58
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