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Reliable GitHub GitHub-Copilot Test Materials, GitHub-Copilot Valid Dumps Pdf

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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

How GitHub Copilot Works and Handles DataThis 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 4

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.

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GitHub CopilotCertification Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
How do you generate code suggestions with GitHub Copilot in the CLI?

A. Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.
B. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.
C. Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.
D. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.

Answer: D

Explanation:
In the CLI, GitHub Copilot generates code suggestions by analyzing code comments. You write comments describing what you want, and Copilot provides relevant code suggestions. You then select the best suggestion from the list.

NEW QUESTION # 67
What is the primary role of the /optimize slash command in Visual Studio?

A. Translates code into a more performant language.
B. Summarizes your documentation into more maintainable and readable formats.
C. Enhances the performance of the selected code by analyzing its runtime complexity.
D. Automatically formats the code according to the selected style guide.

Answer: C

Explanation:
The /optimize slash command in Visual Studio enhances the performance of the selected code by analyzing its runtime complexity and suggesting improvements.

NEW QUESTION # 68
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 innovative coding solutions that are not yet popular.
C. Suggest completely novel projects, while reducing time on a project.
D. Suggest homogeneous solutions if provided a diverse data set.

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 # 69
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

A. Code suggestions made by GitHub Copilot
B. Accepted chat suggestions
C. Suggestions blocked by duplication detection filtering
D. Changes to content exclusion settings

Answer: D

Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.

NEW QUESTION # 70
When can GitHub Copilot still use content that was excluded using content exclusion?

A. If the content exclusion was configured at the enterprise level, and is overwritten at the organization level.
B. When the repository level settings allow overrides by the user.
C. When the user prompts with @workspace.
D. If the contents of an excluded file are referenced in code that is not excluded, for example function calls.

Answer: D

Explanation:
GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.

NEW QUESTION # 71
......

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