GitHub Copilot Exam Dumps & Questions 2026
GitHub Copilot Exam Dumps & Questions 2026
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Question 1:
A developer is concerned about security and privacy while using GitHub Copilot. They want to understand how their code inputs are processed before reaching the AI model and what filters GitHub applies to ensure responsible AI usage. Which of the following best describes the role of GitHub Copilots proxy service and the filters it applies to user prompts?
A. GitHub Copilots proxy service stores all user prompts for 30 days to improve suggestion accuracy.
B. The proxy service removes all identifiable user information, encrypts the prompt, and sends it to OpenAIs Codex without modification.
C. The proxy service applies multiple filters, including length restrictions, sensitive data detection, and spam filtering, before sending the prompt to OpenAIs Codex.
D. The proxy service automatically filters out prompts related to non-code requests but does not analyze security-sensitive patterns.
Answer: C
Explanation:
✅ C. The proxy service applies multiple filters, including length restrictions, sensitive data detection, and spam filtering, before sending the prompt to OpenAI’s Codex.
GitHub Copilot’s proxy service acts as an intermediary that processes user prompts before forwarding them to the AI model (OpenAI’s Codex). It applies various filters to ensure responsible AI usage, such as restricting prompt length, detecting sensitive or personally identifiable information, and filtering out spam or malicious content. This filtering helps protect user privacy and maintain security by preventing unsafe or inappropriate data from reaching the AI model.
❌ A. GitHub Copilot’s proxy service stores all user prompts for 30 days to improve suggestion accuracy.
This is incorrect because GitHub’s documentation does not indicate that the proxy service stores all user prompts for 30 days. While telemetry and usage data may be collected under specific policies, the proxy’s primary role is filtering and forwarding prompts, not long-term storage for accuracy improvement.
❌ B. The proxy service removes all identifiable user information, encrypts the prompt, and sends it to OpenAI’s Codex without modification.
This is inaccurate since the proxy service does more than just remove identifiable information and encrypt data; it actively applies multiple filters and may modify or block prompts that violate policies. It does not simply forward prompts without modification.
❌ D. The proxy service automatically filters out prompts related to non-code requests but does not analyze security-sensitive patterns.
This is false because the proxy service does analyze prompts for sensitive data and applies security-related filters, not just filtering non-code requests. It plays an active role in ensuring responsible AI usage by detecting security-sensitive content.
Question 2:
A software engineer is using GitHub Copilot in Visual Studio Code to generate code suggestions while working on a Python project. At which point in the workflow does the AI model generate a code suggestion?
A. After the developer accepts a previous suggestion and manually submits feedback on its quality
B. After the developer explicitly requests a suggestion by pressing a keyboard shortcut
C. When the developer starts writing a function, even before they finish typing it
D. Only after the developer submits a code snippet to a remote server for validation
Answer: C
Explanation:
✅ C. When the developer starts writing a function, even before they finish typing it
GitHub Copilot is designed to provide real-time code suggestions. It continuously analyzes the context of your code (the surrounding code, comments, and file content) as you type. As soon as you start typing something that gives it enough context—like the beginning of a function definition, a comment indicating intent, or a new line within a method—it will attempt to generate a suggestion, often before you've even finished typing the line or statement. This proactive, inline suggestion is a core feature of Copilot.
❌ A. After the developer accepts a previous suggestion and manually submits feedback on its quality
Accepting a suggestion and submitting feedback are post-suggestion actions. While feedback helps improve the model over time, it's not the trigger for a new code suggestion to be generated. Copilot's generation is an ongoing process based on typing and context.
❌ B. After the developer explicitly requests a suggestion by pressing a keyboard shortcut
While Copilot often provides automatic suggestions, there are indeed keyboard shortcuts to explicitly trigger suggestions (e.g., Ctrl+Enter or Cmd+Enter to open the suggestions panel). However, this option states "After the developer explicitly requests a suggestion," implying it's only through a shortcut. This is incorrect, as Copilot also offers proactive, automatic suggestions as you type, which is its primary mode of operation.
❌ D. Only after the developer submits a code snippet to a remote server for validation
Submitting a code snippet to a remote server for validation sounds like a testing or linting phase, or perhaps a custom service call, not the direct process of code suggestion generation by GitHub Copilot. Copilot generates suggestions locally within your IDE (though it communicates with a remote AI model, the trigger for generation is your local typing activity, not a manual submission of a snippet for validation).
Question 3:
You are a development team leader at a large organization considering purchasing GitHub Copilot Enterprise for your team. You need to understand the key features of the Enterprise plan to determine if it meets your organizations needs. Which of the following features is exclusive to GitHub Copilot Enterprise?
A. The ability to generate documentation for code automatically
B. Centralized management and policy control for Copilot usage across the organization
C. Access to AI-driven code suggestions directly within Visual Studio Code
D. Ability to collaborate on code via GitHub Pull Requests
Answer: B
Explanation:
✅ B. Centralized management and policy control for Copilot usage across the organization
Centralized management and policy control is a key feature exclusive to GitHub Copilot Enterprise. This allows organizations to:
Manage Copilot licenses for their teams.
Enforce organization-wide policies on Copilot behavior, such as preventing Copilot from suggesting code that matches public code.
Gain insights into Copilot usage and adoption across their teams. This level of oversight and control is essential for larger organizations to manage security, compliance, and cost effectively.
❌ A. The ability to generate documentation for code automatically
While GitHub Copilot (even the individual or business plan) can assist with generating documentation (e.g., docstrings for functions, comments explaining code), this is a general capability of the AI model and not a feature exclusive to the Enterprise plan. The AI's ability to understand context and generate explanatory text applies across its different tiers.
❌ C. Access to AI-driven code suggestions directly within Visual Studio Code
Access to AI-driven code suggestions directly within Visual Studio Code (and other supported IDEs like JetBrains IDEs, Neovim, Visual Studio) is the core functionality of GitHub Copilot and is available in all paid tiers (Individual, Business, and Enterprise). It is not exclusive to the Enterprise plan.
❌ D. Ability to collaborate on code via GitHub Pull Requests
Collaborating on code via GitHub Pull Requests is a fundamental feature of GitHub itself, the platform for version control and collaboration. It is not a feature of GitHub Copilot, nor is it exclusive to any specific Copilot plan. Copilot integrates with the development workflow that includes pull requests, but it doesn't provide the pull request functionality itself.
Question 4:
An enterprise is implementing GitHub Copilot with a custom model to improve productivity. Which of the following is a realistic benefit of this setup in an enterprise environment?
A. Custom models allow GitHub Copilot to auto-generate complete project documentation based on recent code changes.
B. It enables GitHub Copilot to suggest complex business logic based on past customer data without any user feedback.
C. Using custom models enables Copilot to automatically fix all syntax errors in the project files.
D. GitHub Copilot can incorporate team-specific patterns and architecture, reducing the need for redundant explanations of common design choices.
Answer: D
Explanation:
✅ D. GitHub Copilot can incorporate team-specific patterns and architecture, reducing the need for redundant explanations of common design choices.
Using custom models in GitHub Copilot Enterprise allows the AI to be fine-tuned on your organization’s own codebase, including proprietary libraries, internal frameworks, and coding standards. This personalization helps Copilot better understand your team’s unique patterns and architecture, making suggestions more relevant and aligned with your established design choices. This reduces repetitive explanations and increases developer productivity by providing context-aware completions tailored to your environment.
❌ A. Custom models allow GitHub Copilot to auto-generate complete project documentation based on recent code changes.
While Copilot can assist with code comments and small documentation snippets, auto-generating complete project documentation is beyond the scope of custom models. Custom models focus on improving code completion relevance rather than generating full documentation automatically.
❌ B. It enables GitHub Copilot to suggest complex business logic based on past customer data without any user feedback.
Custom models improve code suggestions based on your codebase but do not analyze or infer business logic from customer data independently. They require code context and user interaction to provide meaningful suggestions; they do not autonomously generate complex business logic without feedback.
❌ C. Using custom models enables Copilot to automatically fix all syntax errors in the project files.
Copilot can help identify and suggest fixes for some syntax errors during code completion, but it does not automatically fix all syntax errors across project files. Syntax error correction is primarily the role of linters, static analysis tools, or IDE features, not custom AI models.
Question 5:
You are working on a feature for an e-commerce platform and using GitHub Copilot to generate a function for calculating discounts based on user attributes like membership level and purchase history. However, the suggestions are inaccurate. Which prompt crafting best practice can improve the quality of GitHub Copilot‘s suggestions?
A. Providing as little context as possible, trusting Copilot to understand your needs based on minimal input.
B. Only focusing on function names when crafting prompts, as the implementation details will automatically follow.
C. Including clear instructions, comments, and specific variable names to guide Copilots suggestions toward the desired functionality.
D. Writing vague prompts to encourage Copilot to generate more creative and diverse suggestions.
Answer: C
Explanation:
✅ C. Including clear instructions, comments, and specific variable names to guide Copilot’s suggestions toward the desired functionality.
Providing detailed and specific context in your prompts—such as clear instructions, descriptive comments, and meaningful variable names—helps GitHub Copilot understand exactly what you want. This approach guides Copilot to generate more accurate and relevant code suggestions, especially for complex functions like calculating discounts based on user attributes. Best practices in prompt engineering emphasize clarity and specificity to improve AI-generated code quality.
❌ A. Providing as little context as possible, trusting Copilot to understand your needs based on minimal input.
This is incorrect because minimal context often leads to vague or inaccurate suggestions. Copilot performs best when given enough information to understand the problem domain and requirements. Sparse prompts can confuse the model, resulting in less useful code completions.
❌ B. Only focusing on function names when crafting prompts, as the implementation details will automatically follow.
Relying solely on function names is insufficient. While function names help, they do not provide enough detail for Copilot to generate complex logic accurately. Including instructions and comments about expected behavior is necessary to get precise suggestions.
❌ D. Writing vague prompts to encourage Copilot to generate more creative and diverse suggestions.
Vague prompts usually reduce the relevance and correctness of suggestions. Although creativity can be useful, in professional development scenarios—especially involving business logic—specificity and clarity are preferred to ensure correctness and security.
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Question 6:
You are the lead developer at a mid-sized tech company handling both open-source and proprietary code. You need to select the right GitHub Copilot SKU to ensure privacy, security, and collaboration, while complying with internal data security policies. Which SKU best fits your organizations needs?
A. GitHub Copilot for Education
B. GitHub Copilot for Open Source Projects
C. GitHub Copilot for Individuals (Personal Plan)
D. GitHub Copilot for Business
Answer: D
Explanation:
✅ D. GitHub Copilot for Business
GitHub Copilot for Business is the best fit for a mid-sized tech company handling both open-source and proprietary code while needing to ensure privacy, security, collaboration, and compliance with internal data security policies. This SKU provides centralized license management, policy controls, and organizational governance features that help enforce security requirements. It integrates with GitHub Enterprise Cloud and supports organizational controls such as file exclusions, audit logs, and usage policies, which are essential for protecting proprietary code and meeting compliance standards.
❌ A. GitHub Copilot for Education
This SKU is designed specifically for verified students and educators to support learning and teaching scenarios. It does not provide the enterprise-grade security, policy management, or collaboration features required by a mid-sized tech company managing proprietary code.
❌ B. GitHub Copilot for Open Source Projects
This plan is tailored for maintainers of popular open source projects and is generally free or discounted to support open source development. It lacks the organizational management, security controls, and compliance features necessary for enterprises handling proprietary code.
❌ C. GitHub Copilot for Individuals (Personal Plan)
The individual plan is intended for freelancers, hobbyists, or individual developers. It does not include organizational license management, policy enforcement, or IP indemnity features required by companies to ensure compliance and security in collaborative environments.
Question 7:
You are creating an introductory GitHub Copilot tutorial to showcase its ability to work across various popular programming languages in different development environments (e.g., frontend, backend, scripting). Which languages should you include to demonstrate Copilots broad capabilities?
A. Python, Assembly, HTML, Go, Perl
B. Python, Bash, CSS, Swift, R
C. Python, JavaScript, Java, Ruby, HTML
D. Python, HTML, COBOL, C++, Rust
Answer: C
Explanation:
✅ C. Python, JavaScript, Java, Ruby, HTML
This option includes a well-rounded selection of popular programming languages that cover various development environments:
Python for backend and scripting,
JavaScript for frontend and backend (Node.js),
Java for enterprise backend applications,
Ruby for web development and scripting,
HTML for frontend markup.
These languages are widely used and well-supported by GitHub Copilot, making this set ideal to showcase Copilot’s broad capabilities across different programming paradigms and environments.
❌ A. Python, Assembly, HTML, Go, Perl
While Python, HTML, and Go are popular, Assembly and Perl are niche or legacy languages with limited use cases today. Assembly is low-level and less commonly used in modern application development, and Perl is less popular for new projects. This set is less balanced for demonstrating broad, modern development environments.
❌ B. Python, Bash, CSS, Swift, R
This set mixes scripting (Bash), styling (CSS), mobile development (Swift), and statistical computing (R). While diverse, CSS is not a programming language but a styling language, and R is specialized for data science. This combination is less representative of general-purpose programming languages across frontend and backend development.
❌ D. Python, HTML, COBOL, C++, Rust
Including COBOL makes this set less relevant for modern development tutorials since COBOL is mainly used in legacy systems. While C++ and Rust are powerful languages, this mix is skewed toward systems programming rather than broad application development environments.
Question 8:
You are working on a Python script that processes large datasets using Pandas and NumPy. You want to use GitHub Copilot to generate a function that reads a CSV file, filters the rows where a specific column exceeds a threshold, and returns the filtered DataFrame. How would you craft an effective prompt to ensure GitHub Copilot generates code that meets your requirements?
A. “Generate Python code that reads a CSV file, filters rows where the column ‘value‘ is greater than 100, and returns the result as a DataFrame.“
B. “Write a Python function to read and process a CSV file.“
C. “Write a Python function to manipulate data.“
D. “Help me write Python code to analyze data using Pandas.“
Answer: A
Explanation:
✅ A. “Generate Python code that reads a CSV file, filters rows where the column ‘value‘ is greater than 100, and returns the result as a DataFrame.“
This prompt is the most effective because it is specific, clear, and includes all the necessary details for Copilot to generate accurate code:
"Generate Python code": Specifies the language.
"reads a CSV file": Clearly states the input source.
"filters rows where the column 'value' is greater than 100": Provides the exact filtering logic, including the column name and the condition.
"and returns the result as a DataFrame": Specifies the desired output format, which is crucial when working with Pandas.
This level of detail allows Copilot to produce highly relevant and usable code, minimizing the need for manual corrections.
❌ B. “Write a Python function to read and process a CSV file.“
This prompt is too vague. While it specifies the language and the input type (CSV), "process" is ambiguous. It doesn't tell Copilot how to process the file (e.g., what filtering, transformations, or aggregations are needed). This would likely result in a generic function that might only read the file, without any filtering logic.
❌ C. “Write a Python function to manipulate data.“
This prompt is extremely vague. It provides almost no specific context about the input data source, the type of manipulation, or the desired output. Copilot would likely generate a very generic and unhelpful snippet of code, or it might guess incorrectly.
❌ D. “Help me write Python code to analyze data using Pandas.“
While this prompt correctly identifies the language and the library (Pandas), "analyze data" is too broad. It doesn't specify the exact operation (reading, filtering, specific column, threshold). Copilot might suggest code for aggregation, plotting, or other forms of analysis, which would not meet the specific requirement of reading and filtering based on a threshold.
Question 9:
A developer reports that GitHub Copilot is not providing code suggestions in certain files within their Visual Studio Code (VS Code) environment. Which of the following actions is the correct first step to troubleshoot this issue?
A. Verify that Copilot is enabled for the specific file type in the .copilot configuration file.
B. Restart the editor and reinstall GitHub Copilot as this is the only way to restore suggestions.
C. Check if the .gitignore file is preventing Copilot from reading the files contents.
D. Check if GitHub Copilots server is experiencing downtime by reviewing logs in the terminal.
Answer: A
Explanation:
✅ A. Verify that Copilot is enabled for the specific file type in the .copilot configuration file.
When GitHub Copilot is not providing suggestions in specific files, the most common and appropriate first troubleshooting step is to check its configuration. GitHub Copilot has settings that allow users to enable or disable it for specific languages (file types). In Visual Studio Code, this is typically managed in the VS Code settings (either globally or workspace-specific), which ultimately translates to how Copilot processes different file types. If the language of the file (.py for Python, .js for JavaScript, etc.) is disabled for Copilot, no suggestions will appear.
❌ B. Restart the editor and reinstall GitHub Copilot as this is the only way to restore suggestions.
While restarting the editor can sometimes resolve minor glitches, immediately jumping to reinstalling Copilot is an overreaction and rarely the "only way" to restore suggestions. This should be a much later step if basic configuration and connectivity checks fail. It's time-consuming and often unnecessary.
❌ C. Check if the .gitignore file is preventing Copilot from reading the file’s contents.
The .gitignore file is used by Git to tell it which files or directories to ignore from version control. It does not directly prevent GitHub Copilot from reading the file's contents for generating suggestions. Copilot reads the open file in your editor and other relevant files in the project to understand context, regardless of their Gitignored status.
❌ D. Check if GitHub Copilot’s server is experiencing downtime by reviewing logs in the terminal.
While server downtime could be a reason for no suggestions, checking specific terminal logs for server downtime is not the first logical step, especially if the issue is specific files. Server downtime would typically affect all files and users. A more appropriate first check for server status would be the GitHub status page or the Copilot output channel in VS Code for connection errors, rather than general terminal logs. The most likely culprit for specific file issues is configuration.
Question 10:
Which of the following actions can be audited in the GitHub Copilot Business plans audit log to track usage and security events within an organization?
A. Monitoring when a user enables or disables GitHub Copilot within the organizations repository.
B. Logging the specific Copilot training data that was accessed during a suggestion.
C. Viewing and searching for individual code suggestions generated by Copilot for each developer.
D. Recording every instance where Copilot auto-completed a function within the IDE.
Answer: A
Explanation:
✅ A. Monitoring when a user enables or disables GitHub Copilot within the organization’s repository.
The GitHub Copilot Business plan's audit log (accessible via the organization settings on GitHub.com) primarily focuses on administrative and policy-related actions. This includes tracking when Copilot is enabled or disabled at an organizational, team, or user level. It helps administrators ensure compliance with internal policies and manage Copilot's availability for their developers.
❌ B. Logging the specific Copilot training data that was accessed during a suggestion.
This is incorrect. GitHub Copilot's training data is vast and proprietary, used by the underlying AI model. The audit log does not provide visibility into the specific training data points that were accessed by the model when generating a suggestion for a user. This level of detail is not exposed for privacy and intellectual property reasons.
❌ C. Viewing and searching for individual code suggestions generated by Copilot for each developer.
This is incorrect. The audit log in GitHub Copilot Business does not record the specific code suggestions generated by Copilot for each developer. This would be a massive amount of highly sensitive and personal data, raising significant privacy concerns. The audit logs focus on administrative actions, not the content of AI interactions.
❌ D. Recording every instance where Copilot auto-completed a function within the IDE.
This is incorrect. Similar to individual code suggestions, recording every instance of auto-completion would generate an enormous volume of data that is not part of the standard audit logs for the Business plan. The audit logs are designed for high-level administrative tracking and policy enforcement, not for capturing detailed interaction logs at the IDE level for each developer.
For a full set of 810 questions. Go to
https://skillcertpro.com/product/github-copilot-exam-questions-dumps/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.