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| Topic | Details |
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| Topic 1 | - 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 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 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 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 | - 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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Microsoft GitHub Copilot Sample Questions (Q124-Q129):
NEW QUESTION # 124
What role does the pre-processing of user input play in the data flow of GitHub Copilot Chat?
- A. It filters out irrelevant information from the user's input prompt.
- 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 enriches the input prompt with additional context before passing it to the language model.
Answer: D
Explanation:
"Before being sent to the model, user input is pre-processed to enrich the prompt with contextual information such as open files, code selections, or previous conversation history." This shows that pre-processing is used to enrich input with context, making option B correct.
References: GitHub Copilot Chat architecture documentation.
NEW QUESTION # 125
Which of the following statements best describes the impact of GitHub Copilot on the software development process?
- A. It reduces overhead by automating testing workflows.
- B. It replaces the need for developers in the software development process.
- C. It increases productivity by automating repetitive coding tasks.
- D. It decreases software vulnerabilities from third party dependencies.
Answer: C
Explanation:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
Reference: GitHub Copilot impact documentation.
NEW QUESTION # 126
What GitHub Copilot feature can be configured at the organization level to prevent GitHub Copilot suggesting publicly available code snippets?
- A. GitHub Copilot duplication detection filter
- B. GitHub Copilot access to Bing
- C. GitHub Copilot Chat in the IDE
- D. GitHub Copilot Chat in GitHub Mobile
Answer: A
Explanation:
The duplication detection filter can be configured at the organization level to prevent GitHub Copilot from suggesting publicly available code snippets.
NEW QUESTION # 127
Identify the right use cases where GitHub Copilot Chat is most effective. (Each correct answer presents part of the solution. Choose two.)
- A. Create a technical requirement specification from the business requirement documentation
- B. Creation of end-to-end performance testing scenarios for a web application
- C. Explain a legacy COBOL code and translate the code to another language like Python.
- D. Creation of a unit test scenario for newly developed Python code
Answer: C,D
Explanation:
GitHub Copilot Chat is effective for explaining and translating legacy code and generating unit test scenarios for new code.
Reference: GitHub Copilot Chat use cases.
NEW QUESTION # 128
What is a key consideration when relying on GitHub Copilot Chat's explanations of code functionality and proposed improvements?
- A. Reviewing and validating the generated output for accuracy and completeness.
- B. The explanations are dynamically updated based on user feedback.
- C. GitHub Copilot Chat uses a static database for generating explanations.
- D. The explanations are primarily derived from user-provided documentation.
Answer: A
Explanation:
While GitHub Copilot Chat can provide helpful explanations and suggestions, it's crucial to review and validate the generated output. Copilot's suggestions are based on its training data, and they may not always be perfectly accurate or complete. Human judgment is essential to ensure the quality and correctness of the code.
Reference: GitHub Copilot best practices and usage guidelines.
NEW QUESTION # 129
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