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| Section | Weight | Objectives |
|---|
| Topic 1: Prompt Engineering and Context Crafting | | - Effective prompting techniques
- 1. Context optimization for code generation
- 2. Zero-shot and few-shot prompting
|
| Topic 2: Developer Productivity with Copilot | | - Workflow optimization
- 1. Debugging and code completion enhancement
- 2. Using Copilot in IDE workflows
|
| Topic 3: GitHub Copilot Data, Privacy, and Architecture | | - Data handling and privacy safeguards
- 1. Content exclusions and enterprise controls
- 2. Data usage and retention considerations
|
| Topic 4: GitHub Copilot Plans and Features | 31% | - Core Copilot features
- 1. Inline code suggestions
- 2. Chat-based assistance
- 3. CLI and multi-suggestion workflows
- Copilot plans comparison
- 1. Copilot Individual vs Business vs Enterprise
- 2. IDE integration and Copilot Chat usage
|
| Topic 5: Responsible AI | 7% | - Explain responsible usage of AI
- 1. Bias, privacy, security, and transparency considerations
- 2. Ethical AI practices and mitigation of harm
- 3. Risks and limitations of generative AI
- 4. Validating AI-generated output
|
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GitHub CopilotCertification Exam Sample Questions (Q110-Q115):
NEW QUESTION # 110
Where is the proxy service hosted?
- A. Google Cloud Platform
- B. Microsoft Azure
- C. Amazon Web Service
- D. Self hosted
Answer: B
Explanation:
The proxy service for GitHub Copilot is hosted on Microsoft Azure.
NEW QUESTION # 111
When using an IDE with a supported GitHub Copilot plug-in, which Chat features can be accessed from within the IDE? Each correct answer presents part of the solution. (Choose two.)
- A. Explain code and suggest improvements
- B. Find out about releases and commits
- C. Plan coding tasks
- D. Generate unit tests
Answer: A,D
Explanation:
Within your IDE's Copilot Chat pane you can ask it to explain existing code and suggest improvements.
You can also prompt it to generate unit tests for your code directly in the editor.
NEW QUESTION # 112
What role does the pre-processing of user input play in the data flow of GitHub Copilot Chat?
- A. It enriches the input prompt with additional context before passing it to the language model.
- B. It directly generates a response based on the user's input prompt.
- C. It formats the output response before presenting it to the user.
- D. It filters out irrelevant information from the user's input prompt.
Answer: A
Explanation:
During pre-processing, Copilot Chat augments your raw prompt with relevant context--such as system instructions, chat history, and repository metadata--before sending it to the language model for a more informed response.
NEW QUESTION # 113
Which of the following statements best describes the impact of GitHub Copilot on the software development process?
- A. It reduces developer overhead by automating testing workflows.
- B. It decreases software vulnerabilities from third party dependencies.
- C. It increases productivity by automating repetitive coding tasks.
- D. It replaces the need for developers in the software development process.
Answer: C
Explanation:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
NEW QUESTION # 114
Which of the following statements correctly describes how GitHub Copilot Individual uses prompt data? Each correct answer presents part of the solution. (Choose two.)
- A. Prompt data is stored unencrypted for faster processing.
- B. Prompt data is used internally by GitHub for improving the search engine.
- C. Prompt data is used to train machine learning models for better code suggestions.
- D. Real-time user input helps generate context-aware code suggestions.
Answer: C,D
Explanation:
Copilot uses your real‑time input (the prompt and surrounding code) to generate relevant, context
‑aware suggestions on the fly.
Under the Individual plan, unless you opt out, your prompts and accepted suggestions are fed back into GitHub's model‑training pipeline to improve future code completions.
NEW QUESTION # 115
......
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