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GitHub GitHub-Copilot Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering and Context Crafting		- Effective prompting techniques 1. Zero-shot and few-shot prompting 2. Context optimization for code generation
Topic 2: Developer Productivity with Copilot		- Workflow optimization 1. Debugging and code completion enhancement 2. Using Copilot in IDE workflows
Topic 3: Responsible AI	7%	- Explain responsible usage of AI 1. Bias, privacy, security, and transparency considerations 2. Ethical AI practices and mitigation of harm 3. Validating AI-generated output 4. Risks and limitations of generative AI
Topic 4: GitHub Copilot Plans and Features	31%	- Core Copilot features 1. Chat-based assistance 2. CLI and multi-suggestion workflows 3. Inline code suggestions - Copilot plans comparison 1. IDE integration and Copilot Chat usage 2. Copilot Individual vs Business vs Enterprise
Topic 5: GitHub Copilot Data, Privacy, and Architecture		- Data handling and privacy safeguards 1. Data usage and retention considerations 2. Content exclusions and enterprise controls

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GitHub CopilotCertification Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
How can GitHub Copilot assist in maintaining consistency across your tests?

A. By automatically fixing all tests in the code based on the context.
B. By writing the implementation code for the function based on context
C. By providing documentation references based on industry best practices.
D. By identifying a pattern in the way you write tests and suggesting similar patterns for future tests.

Answer: D

Explanation:
GitHub Copilot observes the style and structure of your existing tests and then suggests new tests following those same patterns, helping you keep your testing code consistent.

NEW QUESTION # 87
What content can be configured to be excluded with content exclusions? (Each correct answer presents part of the solution. Choose three.)

A. Folders
B. Lines in files
C. Repositories
D. Files
E. Gists

Answer: A,C,D

Explanation:
Content exclusions allow you to exclude files, folders, and repositories from being used by GitHub Copilot.

NEW QUESTION # 88
How does GitHub Copilot Chat utilize its training data and external sources to generate responses when answering coding questions?

A. It primarily relies on the model's training data to generate responses.
B. It uses user-provided documentation exclusively to generate responses.
C. It combines its training data set, code in user repositories, and external sources like Bing to generate responses.
D. It primarily uses search results from Bing to generate responses.

Answer: C

Explanation:
GitHub Copilot Chat combines its training data, code from user repositories, and external sources like Bing to generate comprehensive and relevant responses to coding questions.

NEW QUESTION # 89
How does GitHub Copilot suggest code optimizations for improved performance?

A. By analyzing the codebase and suggesting more efficient algorithms or data structures.
B. By providing detailed reports on the performance of the codebase.
C. By enforcing strict coding standards that ensure optimal performance.
D. By automatically rewriting the codebase to use more efficient code.

Answer: A

Explanation:
GitHub Copilot suggests code optimizations by analyzing the codebase and recommending more efficient algorithms or data structures.

NEW QUESTION # 90
As a developer who is evaluating the efficiency of GitHub Copilot in generating accurate code across different problem domains. During your R&D, you want to consider the limitations of GenAI tools like Copilot. Which of the following options best describes a core limitation?

A. Copilot always generates error-free code across all languages
B. Copilot reduces the need for STLC and code reviews
C. Copilot can only be used offline without internet access
D. Copilot may return output that reflects bias or inaccurate info because of its training dataright

Answer: D

Explanation:
Copilot is trained on a vast amount of data, which is publicly available; this data may include the biased, outdated, or incorrect practices of code implementation. This typically highlights a key concern in Responsible AI.
References:
https://learn.microsoft.com/en-us/training/modules/responsible-ai-with-github-Copilot/2-manage-ai-risks

NEW QUESTION # 91
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