What method can a developer use to generate sample data with GitHub Copilot? (Each correct
answer presents part of the solution. Choose two.)
A, D
Explanation:
GitHub Copilot can generate sample data by creating fictitious information based on patterns in its
training data and by using suggestions based on API documentation within the repository.
Reference: GitHub Copilot documentation on data generation assistance.
What are the potential risks associated with relying heavily on code generated from GitHub Copilot?
(Each correct answer presents part of the solution. Choose two.)
A, C
Explanation:
Heavy reliance on GitHub Copilot can introduce security vulnerabilities if the generated code
contains known exploits. Additionally, Copilot's suggestions may not always align with best practices
or the latest standards, requiring careful review and validation.
Reference: GitHub Copilot best practices and risk management.
What is the correct way to access the audit log events for GitHub Copilot Business?
C
Explanation:
Audit log events for GitHub Copilot Business can be accessed through the Audit log section within
the organization's GitHub settings. This log provides a record of activities related to Copilot usage
and configuration.
Reference: GitHub Copilot Business documentation on audit logs.
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the
solution. Choose two.)
A, B
Explanation:
Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide
immediate context and adding relevant code snippets directly to your prompts to give Copilot
specific examples and information.
Reference: GitHub Copilot prompt engineering and context management.
How can GitHub Copilot assist developers during the requirements analysis phase of the Software
Development Life Cycle (SDLC)?
B
Explanation:
GitHub Copilot can assist during the requirements analysis phase by providing templates and code
snippets that aid in documenting requirements. This helps streamline the process of capturing and
organizing project requirements.
Reference: GitHub Copilot documentation on SDLC assistance.
How do you generate code suggestions with GitHub Copilot in the CLI?
A
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.
Reference: GitHub Copilot CLI documentation.
Where is the proxy service hosted?
C
Explanation:
The proxy service for GitHub Copilot is hosted on Microsoft Azure.
Reference: GitHub Copilot infrastructure and hosting information.
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP
infringement while using GitHub Copilot?
A
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.
Reference: GitHub Copilot documentation on IP protection and code filtering.
Which principle emphasizes that AI systems should be understandable and provide clear information
on how they work?
B
Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide
clear information about their operations. This ensures that users can understand how the AI arrives
at its decisions and suggestions.
Reference: Microsoft's AI principles and ethical guidelines.
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?
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, 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.
Reference: GitHub Copilot documentation on application modernization.