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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - Privacy Fundamentals and Context Exclusions: This domain focuses on Security Engineers and Compliance Officers and addresses improving code quality with Copilot’s test suggestions and security optimizations. It covers identification of security vulnerabilities, performance enhancements, and privacy features like content exclusions at repository and organization levels with explanation of their limitations. Candidates learn about safeguarding mechanisms such as duplication detection, contractual protections, security checks, and troubleshooting guide for common Copilot issues including context exclusions and suggestion gaps.
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| Topic 3 | - 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.
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| Topic 4 | - 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.
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| Topic 5 | - GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.
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| Topic 6 | - 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.
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Microsoft GitHub Copilot Sample Questions (Q55-Q60):
NEW QUESTION # 55
What are the potential limitations of GitHub Copilot in maintaining existing codebases?
- A. GitHub Copilot can refactor and optimize the entire codebase up to 10,000 lines of code.
- B. GitHub Copilot can independently manage and resolve all merge conflicts in version control.
- C. GitHub Copilot might not fully understand the context and dependencies within a large codebase.
- D. GitHub Copilot's suggestions are always aware of the entire codebase.
Answer: C
Explanation:
"Copilot may not always understand the full context or complex dependencies in large codebases, and suggestions may require significant review." This proves that the main limitation is a lack of deep awareness of complex or large codebase contexts.
References: GitHub Copilot limitations documentation.
NEW QUESTION # 56
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.
Reference: GitHub Copilot documentation on application modernization.
NEW QUESTION # 57
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 # 58
When using GitHub Copilot Chat to generate unit tests, which slash command would you use?
- A. /create-tests
- B. /tests
- C. /generate-tests
- D. /init-tests
Answer: B
Explanation:
"/tests: Generate unit tests for the selected code or function."
This is the official description of the /tests slash command, confirming it is the command used to create unit tests.
References: GitHub Copilot Chat slash command reference.
NEW QUESTION # 59
What is used by GitHub Copilot in the IDE to determine the prompt context?
- A. All the code in the current repository and any git submodules.
- B. All the code visible in the current IDE.
- C. The open tabs in the IDE and the current folder of the terminal.
- D. Information from the IDE like open tabs, cursor location, selected code.
Answer: D
Explanation:
"GitHub Copilot generates suggestions based on context from the IDE, including the file you are editing, open tabs, cursor position, and any selected code." This proves that the context is drawn from editor state information, making option A correct.
References: GitHub Copilot context and suggestions documentation.
NEW QUESTION # 60
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