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| Topic | Details |
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| Topic 1 | - How GitHub Copilot Works and Handles Data: Designed for Machine Learning Engineers and Data Privacy Specialists, this section covers the data lifecycle and processing behind Copilot’s code suggestions. It explains how context is gathered, prompts constructed, responses generated, and post-processed through proxy services. Candidates understand Copilot’s data policies, handling of inputs, and limitations such as context window size and data age influencing suggestion relevance.
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| Topic 2 | - 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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| 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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Microsoft GitHub Copilot Sample Questions (Q105-Q110):
NEW QUESTION # 105
What types of prompts or code snippets might be flagged by the GitHub Copilot toxicity filter? (Each correct answer presents part of the solution. Choose two.)
- A. Code comments containing strong opinions or criticisms
- B. Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)
- C. Code that contains logical errors or produces unexpected results
- D. Sexually suggestive or explicit content
Answer: B,D
Explanation:
GitHub Copilot includes a toxicity filter to prevent the generation of harmful or inappropriate content. This filter flags prompts or code snippets that contain hate speech, discriminatory language, or sexually suggestive or explicit content. This ensures a safe and respectful coding environment.
Reference: GitHub Copilot documentation on safety and content filtering.
NEW QUESTION # 106
You use GitHub Copilot to develop a web app named App1 in a repository named Repo1.
Repo1 contains generated files and supporting artifacts.
You discover that GitHub Copilot suggestions in the IDE are being generated based on files that are NOT part of the App1 source code.
You need to ensure that GitHub Copilot ignores these files.
What should you do?
- A. Disable GitHub Copilot inline suggestions in the IDE.
- B. Configure Copilot content exclusion rules by modifying the GitHub Copilot settings of Repo1.
- C. Add the generated files and supporting artifacts to the .gitignore file of Repo1.
- D. Add repository custom instructions telling GitHub Copilot not to use the generated files.
Answer: B
Explanation:
GitHub Copilot content exclusion is the feature designed to prevent specified repository files or directories from being used as Copilot context. GitHub ' s documentation states that repository administrators can configure exclusions in the repository ' s settings under Copilot and Content exclusion. When content is excluded, it does not inform inline suggestions in other files, which directly addresses the problem of generated or supporting artifacts influencing suggestions for App1. A .gitignore file controls Git version tracking behavior, not Copilot context. Disabling inline suggestions would remove the desired feature rather than fix its context, and repository custom instructions are guidance rather than an access restriction.
Therefore, configure Copilot content exclusion rules in the GitHub Copilot settings for Repo1.
NEW QUESTION # 107
How does GitHub Copilot assist developers in reducing the amount of manual boilerplate code they write?
- A. By refactoring the entire codebase to eliminate boilerplate code without developer input.
- B. By engaging in real-time collaboration with multiple developers to write boilerplate code.
- C. By predicting future coding requirements and pre-emptively generating boilerplate code.
- D. By suggesting code snippets that can be reused across different parts of the project.
Answer: D
Explanation:
"Copilot helps reduce boilerplate by suggesting reusable code snippets for repetitive structures across your project." This proves that Copilot saves time by generating reusable snippets, not by preemptively or automatically refactoring entire codebases.
References: GitHub Copilot productivity documentation.
NEW QUESTION # 108
What content can be configured to be excluded with content exclusions? (Each correct answer presents part of the solution. Choose three.)
- A. Gists
- B. Files
- C. Repositories
- D. Folders
- E. Lines in files
Answer: B,C,D
Explanation:
Content exclusions allow you to exclude files, folders, and repositories from being used by GitHub Copilot.
NEW QUESTION # 109
In what ways can GitHub Copilot contribute to the design phase of the Software Development Life Cycle (SDLC)?
- A. GitHub Copilot can generate user interface (UI) prototypes without prompting.
- B. GitHub Copilot can manage design team collaboration and version control.
- C. GitHub Copilot can suggest design patterns and best practices relevant to the project.
- D. GitHub Copilot can independently create a complete software design.
Answer: C
Explanation:
"Copilot can assist in the design phase by suggesting design patterns, frameworks, and best practices relevant to the context of your project." This shows Copilot contributes by offering design-related recommendations, not by independently producing full designs or managing collaboration.
References: GitHub Copilot use case documentation.
NEW QUESTION # 110
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