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Microsoft GH-300 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	GitHub Copilot
Exam Number:	GH-300
Exam Format:	Scenario-based, Multiple-choice, Proctored
Exam Duration:	100 minutes
Certificate Validity Period:	1 year
Available Languages:	Portuguese (Brazil), Spanish, Japanese, German, Korean, English, Arabic (Saudi Arabia), Chinese (Simplified), French
Exam Price:	$99 USD
Passing Score:	700 / 1000
Real Exam Qty:	60–65
Recommended Training:	Microsoft Learn: GitHub Copilot Learning Path
Exam Registration:	Microsoft Learn Exam Details Pearson VUE Registration
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	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 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	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 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 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.

Microsoft GitHub Copilot Sample Questions (Q98-Q103):

NEW QUESTION # 98
Which of the following describes role prompting?

A. Describing in your prompt what your role is to get a better suggestion
B. Tell GitHub Copilot in what tone of voice it should respond
C. Prompt GitHub Copilot to explain what was the role of a suggestion
D. Giving GitHub Copilot multiple examples of the form of the data you want to use

Answer: A

Explanation:
Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions. By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide Copilot to tailor its responses to align with the expertise and perspective associated with that role. This improves the quality and relevance of the generated code and explanations.
Reference: GitHub Copilot documentation on prompt engineering and best practices.

NEW QUESTION # 99
What is a limitation of content exclusions?

A. Content exclusions can only be configured by an enterprise administrator.
B. Repository administrators and organization owners cannot manage content exclusion settings.
C. Content exclusions are only available in the GitHub Copilot Individual plan.
D. Content exclusions can be worked around as it is only available for Git repositories.

Answer: D

Explanation:
A limitation is that content exclusions are only available for Git repositories, meaning they can be worked around if content is accessed through other means.

NEW QUESTION # 100
You start GitHub Copilot CLI from a directory named /repo/app. You store test files in a directory named
/repo/tests.
You need to add /repo/tests as a trusted directory without restarting GitHub Copilot CLI. The solution must ensure that GitHub Copilot can access and modify the files.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 101
effectiveness of GitHub Copilot? (Each correct answer presents part of the solution. Choose two.) 1
1.
blog.yatricloud.com
blog.yatricloud.com

A. The API can provide feedback on coding style and standards compliance.
B. The API can generate detailed reports on code quality improvements made by GitHub Copilot.
C. The API can track the acceptance rate of code suggestions accepted and used in the organization.
D. The API can provide Copilot Chat specific suggestions acceptance metrics.
E. The API can refactor your code to improve productivity.

Answer: C,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.

NEW QUESTION # 102
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

A. GitHub Copilot can independently verify the statistical significance of results.
B. GitHub Copilot can design new statistical methods that have not been previously documented.
C. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.
D. GitHub Copilot will automatically correct any statistical errors found in the user's initial code.

Answer: C

Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.
Reference: GitHub Copilot data analysis limitations.

NEW QUESTION # 103
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