Three Formats Of Latest GitHub-Copilot Exam Questions

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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	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.
Topic 4	GitHub Copilot Plans and FeaturesThis section of the exam measures the skills of Software Engineers and IT Administrators and covers different GitHub Copilot plans, including Individual, Business, and Enterprise editions. It explains the integration of GitHub Copilot within IDEs and discusses key features such as inline chat, multiple suggestions, and exception handling. The section details the policies for managing GitHub Copilot within organizations, including auditing logs and API management. It also highlights advanced functionalities like knowledge bases for improved code quality and best practices for Copilot Chat usage.
Topic 5	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.

GitHub CopilotCertification Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
What is a likely effect of GitHub Copilot being trained on commonly used code patterns?

A. Suggest code snippets that reflect the most common practices in the training data.
B. Suggest completely novel projects, while reducing time on a project.
C. Suggest homogeneous solutions if provided a diverse data set.
D. Suggest innovative coding solutions that are not yet popular.

Answer: A

Explanation:
GitHub Copilot is likely to suggest code snippets that reflect common practices in its training data, as it learns from frequently used patterns.

NEW QUESTION # 55
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?

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

Answer: B

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,Bis 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 # 56
Which of the following is a risk associated with using AI?

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

Answer: B

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 # 57
What is a benefit of using custom models in GitHub Copilot?

A. Responses use practices and patterns in your repositories
B. Responses are guaranteed to be correct
C. Responses are faster to produce and appear sooner
D. Responses use the organization's LLM engine

Answer: A

Explanation:
Custom models in GitHub Copilot allow the tool to learn from the specific code patterns and practices within your repositories. This results in suggestions that are more aligned with your organization's coding standards and conventions, improving the relevance and accuracy of the generated code.

NEW QUESTION # 58
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?

A. You need to check code suggestions yourself before accepting
B. Suggestions matching public code to 'blocked'
C. Enforce blocking of MIT or GPL licensed code
D. Enable GitHub Copilot license checking

Answer: B

Explanation:
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.

NEW QUESTION # 59
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Three Formats Of Latest GitHub-Copilot Exam Questions

High Pass-Rate GitHub-Copilot Pdf Version & Accurate GitHub-Copilot Current Exam Content: GitHub CopilotCertification Exam

GitHub GitHub-Copilot Exam Syllabus Topics:

GitHub CopilotCertification Exam Sample Questions (Q54-Q59):

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