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GitHub-Copilot New Braindumps Sheet & GitHub-Copilot Study Dumps

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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

Responsible AI: This section of the exam measures the skills of AI Ethics Analysts and AI Developers and covers the principles of responsible AI usage, the risks associated with AI, and the limitations of generative AI tools. It includes the importance of validating AI-generated outputs and operating AI systems responsibly. It also explores potential harms such as bias, privacy concerns, and fairness issues, along with methods to mitigate these risks. The ethical considerations of AI development and deployment are also discussed.

Topic 2

Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.

Topic 3

Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.

Topic 4

How GitHub Copilot Works and Handles Data: This section of the exam measures the skills of Data Security Specialists and DevOps Engineers and covers how GitHub Copilot processes data, handles code suggestions and manages privacy concerns. It explains the data pipeline for Copilot’s suggestions, how it gathers context, and how prompts are processed through its AI model. The section also discusses the limitations of AI-generated code, the effects of historical data on suggestions, and the role of prompt crafting. Best practices for improving prompt effectiveness and optimizing AI-generated responses are included.

Topic 5

Developer Use Cases for AI: This section of the exam measures skills of Full-Stack Developers and Cloud Engineers and covers how AI enhances developer productivity across various tasks such as learning new programming languages, debugging, writing documentation, and refactoring code. It discusses how GitHub Copilot integrates with the Software Development Lifecycle (SDLC) and its role in modernizing legacy applications. It also highlights the use of AI for personalized responses, sample data generation, and improving overall efficiency in software development.

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GitHub CopilotCertification Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution. Choose two.)

A. Monthly as a subscription
B. Free (not billed) for all open source projects
C. Annually as a subscription
D. Monthly, as a metered service based on actual consumption

Answer: A,C

Explanation:
GitHub Copilot Individual is billed as a monthly or annual subscription.

NEW QUESTION # 22
What is a limitation of content exclusions?

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

Answer: B

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 # 23
Which of the following is a risk associated with using AI?

A. AI replaces the need for developer opportunities in most fields.
B. AI eliminates the need for data privacy regulations.
C. AI algorithms are incapable of perpetuating existing biases.
D. AI systems can sometimes make decisions that are difficult to interpret.

Answer: D

Explanation:
A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.

NEW QUESTION # 24
How can GitHub Copilot facilitate a smoother learning experience when diving into a new programming language? (Each correct answer presents part of the solution. Choose two.)

A. GitHub Copilot's /understand command will help GitHub Copilot to understand code written in a targeted programming language.
B. GitHub Copilot Chat can provide guidance and support for common coding tasks and challenges in the targeted programming language.
C. GitHub Copilot can convert comments into code to grasp the syntax and nuances of a new programming language.
D. GitHub Copilot can provide contextualized code suggestions and answer sources from an organization's documentation.

Answer: B,C

Explanation:
GitHub Copilot helps with learning new languages by providing guidance on common tasks and by converting comments into code, allowing developers to see syntax in action.

NEW QUESTION # 25
An independent contractor develops applications for a variety of different customers. Assuming no concerns from their customers, which GitHub Copilot plan is best suited?

A. GitHub Copilot Business
B. GitHub Copilot Teams
C. GitHub Copilot Business for non-GHE Customers
D. GitHub Copilot Enterprise
E. GitHub Copilot Individual

Answer: E

Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.

NEW QUESTION # 26
......

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