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| Topic 1 | - 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 2 | - 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 3 | - 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 4 | - 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 5 | - 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 (Q57-Q62):
NEW QUESTION # 57
How can the insights gained from the metrics API be used to improve the development process in conjunction with GitHub Copilot?
- A. Detailed analysis of GitHub Copilot's suggestions vs. manual coding.
- B. Automated generation of complete project documentation.
- C. Real-time debugging and error resolution statistics.
- D. Insights on the types of coding languages where GitHub Copilot is most helpful.
Answer: A
Explanation:
"The Copilot metrics API provides insights into how suggestions are being accepted and used, enabling teams to analyze the relative impact of Copilot-generated code versus manually written code." This confirms option D is correct, as the API enables analysis of Copilot versus manual coding.
References: GitHub Copilot metrics API documentation.
NEW QUESTION # 58
A company is currently storing code in Bitbucket and would like to use GitHub Copilot. Which GitHub Copilot plan will be most cost effective to allow them to manage users with their Identity Provider (e.g. Okta)?
- A. GitHub Copilot Enterprise
- B. GitHub Copilot Business for non-GHE customers
- C. GitHub Copilot Individual
- D. GitHub Copilot Teams
Answer: A
Explanation:
GitHub Copilot Enterprise is the most cost-effective plan for managing users with an Identity Provider like Okta, as it provides enterprise-level features and integration.
Reference: GitHub Copilot pricing and features.
NEW QUESTION # 59
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?
- A. GitHub Copilot will automatically correct any statistical errors found in the user's initial code.
- B. GitHub Copilot can design new statistical methods that have not been previously documented.
- C. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.
- D. GitHub Copilot can independently verify the statistical significance of results.
Answer: C
Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.
NEW QUESTION # 60
How long does it take content exclusion to add or be updated?
- A. Up to 30 minutes
- B. 45 - 60 minutes
- C. 24 hours
- D. 60 - 90 minutes
Answer: A
Explanation:
Content exclusions typically take up to 30 minutes to be added or updated.
Reference: GitHub Copilot content exclusion documentation.
NEW QUESTION # 61
What is a likely effect of GitHub Copilot being trained on commonly used code patterns?
- A. Suggest innovative coding solutions that are not yet popular.
- B. Suggest code snippets that reflect the most common practices in the training data.
- C. Suggest completely novel projects, while reducing time on a project.
- D. Suggest homogeneous solutions if provided a diverse data set.
Answer: B
Explanation:
GitHub Copilot is likely to suggest code snippets that reflect common practices in its training data, as it learns from frequently used patterns.
NEW QUESTION # 62
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