20 Tough GitHub Copilot Certification Exam Questions & Answers

GitHub Copilot Certification Exam Questions

Over the few months, I’ve been working hard to help professionals who’ve found themselves displaced by the AI revolution discover new and exciting careers in tech.

Part of that transition is building up an individual’s resume, and the IT certification I want all of my clients to put at the top of their list is the Copilot certified designation from GitHub.

Whether you’re a Scrum Master, Business Analyst, DevOps engineer, or senior software developer, the first certification I recommend is the GitHub Copilot certification.

You simply won’t thrive in the modern IT landscape if you can’t prompt your way out of a paper bag. The truth is, every great technologist today needs to understand how to use large language models, master prompting strategies, and work confidently with accelerated code editors powered by AI.

That’s exactly what the GitHub Copilot Exam measures. It measures your ability to collaborate intelligently with AI to write, refactor, and optimize code at an expert level.

GitHub Copilot exam simulators

Through my Udemy courses on Git, GitHub, and GitHub Copilot, and through my free practice question banks at certificationexams.pro, I’ve seen firsthand which topics challenge learners the most. Based on thousands of student interactions and performance data, these are 20 of the toughest GitHub Copilot certification exam questions currently circulating in the practice pool.

You can find more GitHub Copilot Practice Questions in Udemy course.

Each question is thoroughly answered at the end of the set, so take your time, think like a Copilot, and check your reasoning once you’re done.

If you’re preparing for the GitHub Copilot Exam or exploring other certifications from AWS, GCP, or Azure, you’ll find hundreds more free practice exam questions and detailed explanations at certificationexams.pro.

And note, these are not GitHub Copilot exam dumps or braindumps. These are all original questions that will prepare you for the exam by teaching you not only what is covered, but also how to approach answering exam questions. That’s why each answer comes with it’s own tip and guidance.

Now, let’s dive into the 20 toughest GitHub Copilot certification exam questions. Good luck, and remember, every great career in the age of AI begins with mastering how to prompt.

GitHub Copilot Practice Exam Questions

Question 1

You are the platform administrator for the engineering organization at mcnz.com that uses GitHub Copilot Business, and you want to run an automated audit every 90 days to see which members currently hold active seats. Using GitHub’s REST API, which endpoint and HTTP method will return the active Copilot seat assignments for your organization, and what OAuth scope must the token include?

• A. Use GET /enterprises/{enterprise}/copilot/licenses and the API token must include admin:enterprise scope
• B. Call GET /orgs/{org}/copilot/licenses with a token that has admin:org scope
• C. Use GET /orgs/{org}/copilot/subscriptions and the API token must include admin:org scope
• D. Send POST /orgs/{org}/copilot/assign-license and the API token must include write:org scope

Question 2

At Northwind Labs an engineer is using GitHub Copilot to implement a pricing routine that must maintain 30 decimal places of accuracy. Copilot does well at proposing code snippets yet you are unsure about its ability to reason and to guarantee precise results. Which statement best reflects what Copilot can and cannot do in this situation?

• A. With Vertex AI integration, GitHub Copilot can reason through formal proofs and ensure that the generated code is mathematically sound
• B. GitHub Copilot can write code that performs calculations, yet it relies on the logic you request and it does not inherently understand or assure the accuracy of those results
• C. GitHub Copilot is purpose built for mathematical reasoning and can replace specialized tools like Wolfram Alpha or scientific calculator apps
• D. GitHub Copilot performs complex calculations with guaranteed precision because its deep learning model is optimized for numeric computation

Question 3

In an IDE, what context does GitHub Copilot use to build the prompt that powers its code completion suggestions?

• A. It performs a cloud semantic search over repository embeddings before every suggestion
• B. It uploads the entire workspace to the model so it can use full project context
• C. It constructs the prompt from the nearby code around the cursor including visible content comments and signatures in the current file
• D. It relies solely on the function name at the caret

Question 4

You manage the platform team at Riverton Apps with around 90 developers. Your organization recently rolled out GitHub Copilot Business to improve day to day coding, yet several teammates remain unclear about what differentiates this plan from other Copilot tiers. When you are asked to call out what GitHub Copilot Business actually provides, which capability should you include?

• A. Automatic merge conflict resolution during pull requests
• B. Enterprise security and compliance such as SOC 2 Type II and GDPR coverage
• C. Native integration with your own private cloud hosted AI models
• D. Free GitHub Actions hosted runners for all workflows

Question 5

Your engineering group is participating in an early access evaluation of GitHub Copilot Chat and you have been asked to send thorough feedback that covers how the assistant uses context, the quality of the conversation flow, and any problems you encounter during everyday coding. Which channel should you use to share detailed feedback so it reaches the Copilot Chat team?

• A. GitHub Community Discussions for Copilot
• B. Use the feedback control in your supported IDE to submit Copilot Chat specific feedback
• C. Email GitHub Support with your account details and a thorough description
• D. Open an issue in the official GitHub Copilot repository

Question 6

What is the best way to ensure GitHub Copilot suggestions are accessible for a developer who uses a screen reader?

• A. Use GitHub.dev in the browser
• B. Use an IDE with robust screen reader support and verified Copilot compatibility
• C. Turn off Copilot

Question 7

Your team at example.com uses GitHub Copilot in a monorepo and notices that its completions frequently mirror widely used idioms from well known libraries and frameworks. How can the dominance of “most seen” examples in the training data shape the suggestions and what kinds of problems could that create?

• A. Using Cloud Code enforces project conventions so Copilot suggestions are immune to training data bias
• B. The model always produces novel solutions and does not rely on prior examples
• C. Copilot tends to favor patterns it has encountered most often, which can be a poor fit for unusual or innovative code needs
• D. Frequently observed patterns guarantee the most optimal and efficient code for any context

Question 8

At BrightWave Studios you oversee 72 engineers who use Copilot Business each day. Executives want a monthly summary that shows how frequently Copilot is used with a breakdown by programming language and by code editor so they can evaluate return on investment. What approach should you take to generate this report?

• A. Enable Dependabot alerts and infer Copilot usage from the security dashboard
• B. Ask each developer to export editor logs and manually consolidate usage
• C. Use the GitHub REST API to pull organization Copilot usage metrics
• D. Cloud Monitoring

Question 9

A fintech team at example.com is building an analytics service that handles customer identifiers and other personally identifiable information. As the team enables GitHub Copilot, they want to make sure sensitive files and comments are not included in Copilot’s context for code suggestions because Copilot reads local content to generate prompts. What is the best way to keep this information out of Copilot’s context?

• A. Temporarily disable GitHub Copilot while working on sections that contain confidential data
• B. Configure a .copilotignore file to prevent sensitive files and directories from being sent as context to GitHub Copilot
• C. Use GitHub Advanced Security secret scanning to detect exposure of sensitive tokens in the repository
• D. Replace real identifiers with masked sample values in comments and sample data before using Copilot

Question 10

A development team at CanyonWorks Analytics uses GitHub Copilot to speed up delivery of a new API that will run on Cloud Run and connect to Cloud SQL. They intend to move this service to production within 45 days. What is a responsible practice when integrating Copilot generated code into the production service?

• A. Adopt Copilot suggestions without changes if the code compiles and unit tests succeed
• B. Turn off telemetry and feedback collection to address privacy concerns
• C. Depend on Cloud Armor and VPC Service Controls to offset any insecure code patterns
• D. Perform code review and run security and license checks on Copilot generated code before promotion to production

Question 11

Priya is a backend engineer at scrumtuous.com who uses GitHub Copilot Individual in IntelliJ IDEA, and she wants to understand how suggestions appear while typing and whether she can request alternative completions on demand. Which statement correctly describes how GitHub Copilot Individual behaves inside a supported IDE?

• A. GitHub Copilot only works when the project is stored in a GitHub repository and cannot assist in local folders
• B. GitHub Copilot never provides inline suggestions automatically and only activates with a specific key press
• C. GitHub Copilot offers inline completions as you type and lets you browse alternate suggestions using keyboard shortcuts
• D. GitHub Copilot automatically creates full applications without developer prompts and does not need review

Question 12

How does GitHub Copilot process editor code snippets to maintain confidentiality and avoid retaining them?

• A. GitHub Copilot runs only within the IDE so no code ever leaves the machine
• B. GitHub Copilot forwards snippets to Azure OpenAI and keeps anonymized prompts in logs for 30 days
• C. GitHub Copilot sends minimal editor context to the service for a real time suggestion and it discards the snippet after responding
• D. GitHub Copilot encrypts snippets in transit and at rest and it stores them indefinitely for future training

Question 13

A developer at mcnz.com is working in Visual Studio Code with GitHub Copilot enabled and needs to ask for an explanation of a complex function and get context about the surrounding code without leaving the editor, which capability should they use?

• A. GitHub Copilot Inline Suggestions
• B. GitHub Copilot CLI
• C. GitHub Copilot Chat
• D. GitHub Copilot Multiple Suggestions

Question 14

At mcnz.com a developer is using GitHub Copilot to draft a Python function that returns the largest number in a list. Which prompt wording would most likely lead Copilot to produce correct and concise code?

• A. Cloud Functions
• B. Compute the average of the numbers
• C. Generate a Python function that returns the maximum number from a list input
• D. Compare things in Python

Question 15

Your engineering team at NovaStream uses WebStorm and GoLand alongside Visual Studio Code for daily development. You plan to roll out GitHub Copilot in a consistent way across both environments. What should you do in the JetBrains IDEs to make sure Copilot integrates smoothly into the team’s workflow?

• A. Rely on JetBrains’ built in code completion features instead of GitHub Copilot
• B. Install the Cloud Code plugin for JetBrains and configure it for the project to provide AI assisted development
• C. Install the GitHub Copilot plugin from JetBrains Marketplace then sign in with your GitHub account and have each developer enable it for their IDE and project
• D. Copy suggestions from Visual Studio Code into JetBrains editors because Copilot does not run natively in JetBrains

Question 16

At scrumtuous.com you are developing a TypeScript service in Visual Studio Code with “GitHub Copilot” enabled and you want to understand how suggestions are generated and whether any of your code is retained by the service. Which description best matches the “Copilot” data lifecycle for your prompts and completions?

• A. All code analysis and suggestion generation run only on your laptop and no data is ever sent to any remote service
• B. Snippets from your editor are regularly captured and stored to continuously retrain the Copilot model
• C. Your editor content is processed only in memory to produce suggestions and the Copilot model runs on GitHub servers trained on public repositories and your code is neither stored nor logged
• D. GitHub Copilot uses Vertex AI Codey in your Google Cloud project to host the model and prompts are stored in your project for future fine tuning

Question 17

An engineer at mcnz.com maintains a Python endpoint running on Cloud Run that relies on a naive recursive Fibonacci function which is correct but becomes very slow when n grows. You want to use GitHub Copilot to surface performance issues and to propose code changes while also improving the test suite. What should you ask Copilot to do to optimize the implementation and generate more effective tests?

• A. Cloud Profiler
• B. Tell Copilot to create stress tests for extremely large values like n equals 2500 and then ignore any refactoring suggestions it provides
• C. Request that Copilot refactor the function using memoization or an iterative dynamic programming method and produce tests that cover larger input sizes
• D. Have Copilot rewrite the routine with deeper recursive branching and write tests only around that new recursion in the hope that performance will improve automatically

Question 18

Which GitHub Copilot plan provides enterprise level governance, security, and compliance for teams that work in both public and private repositories?

• A. Copilot Business
• B. GitHub Advanced Security
• C. Copilot Enterprise
• D. Copilot Individual

Question 19

You use an AI coding assistant in your IDE to generate Python and you want it to produce a PyTorch function that trains a neural network with higher accuracy. You have two short snippets from a previous project that show how you load data and define the model, and you plan to paste them before your request so you can apply few-shot prompting. How should you word your prompt so the assistant follows your examples and completes the training function?

• A. Write PyTorch training code and I will provide examples later
• B. Use Vertex AI to build and train the model instead of writing PyTorch code
• C. Implement a PyTorch training function and use the two example snippets below that show data loading and model setup as guidance
• D. Create a function that uses PyTorch to train a neural network

Question 20

You are the platform owner at a media analytics startup that deployed GitHub Copilot for Business. Engineers are concerned that Copilot could surface snippets from confidential repositories and leadership wants to ensure private code never contributes to training. You intend to update the Copilot editor configuration so both suggestions and training exclude private code. Which configuration should you apply?

• A. Cloud DLP
• B. Enable “useBusinessRules” to true so organization privacy controls filter out internal code in suggestions
• C. Configure the “excludePatterns” rule in the Copilot editor config to ignore selected private repositories or directories during completions
• D. Turn on “corporateDataProtection” so proprietary code is safeguarded by enterprise privacy settings
• E. Set “trainingData” to false in the Copilot editor configuration so private code is not used for model learning

GitHub Copilot Exam Answers

Check out my other GitHub certification courses on Udemy!

Question 1

You are the platform administrator for the engineering organization at mcnz.com that uses GitHub Copilot Business, and you want to run an automated audit every 90 days to see which members currently hold active seats. Using GitHub’s REST API, which endpoint and HTTP method will return the active Copilot seat assignments for your organization, and what OAuth scope must the token include?

• [*] B. Call GET /orgs/{org}/copilot/licenses with a token that has admin:org scope

The correct option is Call GET /orgs/{org}/copilot/licenses with a token that has admin:org scope.

This endpoint is designed to list Copilot seat assignments for a specific organization, which matches the need to audit who currently holds seats in your org. It requires an organization administrator token with the appropriate scope because viewing and managing seat assignments is an admin function. The response provides per user license information that you can use to identify active seats and automate your 90 day audit.

Use GET /enterprises/{enterprise}/copilot/licenses and the API token must include admin:enterprise scope is not correct because it targets enterprise wide assignments rather than a single organization. The question asks for an organization level audit in Copilot Business so the enterprise level endpoint and scope do not apply.

Use GET /orgs/{org}/copilot/subscriptions and the API token must include admin:org scope is not correct because that path does not return seat assignments and is not a documented endpoint for listing Copilot seats.

Send POST /orgs/{org}/copilot/assign-license and the API token must include write:org scope is not correct because it performs assignment rather than retrieval and it uses the wrong method and scope for a read operation.

Exam Tip

Match the resource level to the question by looking for words like org or enterprise and verify the HTTP method aligns with the task. For read only audits prefer GET endpoints and ensure the token has the required admin scope.

Question 2

At Northwind Labs an engineer is using GitHub Copilot to implement a pricing routine that must maintain 30 decimal places of accuracy. Copilot does well at proposing code snippets yet you are unsure about its ability to reason and to guarantee precise results. Which statement best reflects what Copilot can and cannot do in this situation?

• [*] B. GitHub Copilot can write code that performs calculations, yet it relies on the logic you request and it does not inherently understand or assure the accuracy of those results

The correct option is GitHub Copilot can write code that performs calculations, yet it relies on the logic you request and it does not inherently understand or assure the accuracy of those results.

This is correct because Copilot generates code suggestions from patterns in training data and from your prompts. It does not execute code to validate outputs and it does not perform formal verification. If you need 30 decimal places of accuracy then you must choose appropriate numeric types or arbitrary precision libraries and you must write tests to confirm correctness. Copilot can help draft such code and tests, yet you remain responsible for specifying the algorithm and verifying precision.

With Vertex AI integration, GitHub Copilot can reason through formal proofs and ensure that the generated code is mathematically sound is wrong because Copilot does not perform formal proofs and it offers no guarantee of mathematical soundness. There is no feature that turns Copilot into a formal verification system and integration with another AI platform does not change that limitation.

GitHub Copilot is purpose built for mathematical reasoning and can replace specialized tools like Wolfram Alpha or scientific calculator apps is wrong because Copilot is a code completion and chat assistant. It is not a computer algebra system or a high precision computational engine and it does not replace specialized math tools.

GitHub Copilot performs complex calculations with guaranteed precision because its deep learning model is optimized for numeric computation is wrong because language models generate text and code rather than compute with guaranteed numeric precision. Copilot does not guarantee the accuracy of calculations and it does not optimize for exact arithmetic.

Exam Tip

When options claim an AI assistant can guarantee correctness or provide formal proofs then prefer the statement that emphasizes generation and the need for your own validation and tests. Treat words like ensure and guarantee as red flags for tools that suggest code.

Question 3

In an IDE, what context does GitHub Copilot use to build the prompt that powers its code completion suggestions?

• [*] C. It constructs the prompt from the nearby code around the cursor including visible content comments and signatures in the current file

The correct option is It constructs the prompt from the nearby code around the cursor including visible content comments and signatures in the current file.

In an IDE, Copilot forms completion prompts from the local editing context so it looks at the text around the caret in the active file. It uses visible code, comments, documentation strings, and function or method signatures to infer intent and suggest relevant completions. This approach keeps prompts focused and responsive while preserving privacy by limiting what is sent.

It performs a cloud semantic search over repository embeddings before every suggestion is incorrect because inline completions rely on the local editor context and are not driven by a repository wide embedding query before each suggestion.

It uploads the entire workspace to the model so it can use full project context is incorrect because Copilot does not transmit your whole project for each suggestion and instead sends only the necessary snippets from the active editing context to build the prompt.

It relies solely on the function name at the caret is incorrect because Copilot considers the surrounding code and comments in the current file and not just a single symbol name.

Exam Tip

When options describe extremes, such as uploading the whole workspace or using only a single token, prefer the answer that uses local context near the cursor and the active file since that aligns with how IDE completions are typically powered.

Question 4

You manage the platform team at Riverton Apps with around 90 developers. Your organization recently rolled out GitHub Copilot Business to improve day to day coding, yet several teammates remain unclear about what differentiates this plan from other Copilot tiers. When you are asked to call out what GitHub Copilot Business actually provides, which capability should you include?

• [*] B. Enterprise security and compliance such as SOC 2 Type II and GDPR coverage

The correct capability to include is Enterprise security and compliance such as SOC 2 Type II and GDPR coverage.

Copilot Business is designed for organizations that need strong security assurances and documented compliance. It offers enterprise grade controls and data handling commitments that align with common audit and privacy expectations, which is why this plan is positioned for teams that must satisfy rigorous standards.

Automatic merge conflict resolution during pull requests is not a feature of Copilot. While Copilot can suggest code in editors and chat, it does not automatically resolve merge conflicts in pull requests and developers must still review and resolve those conflicts.

Native integration with your own private cloud hosted AI models is not part of Copilot Business. Copilot Business uses GitHub managed models hosted by Microsoft and does not support bring your own model integration as a native capability.

Free GitHub Actions hosted runners for all workflows is not included with Copilot Business. Actions usage and runners are billed under GitHub Actions pricing and quotas that are separate from Copilot licensing.

Exam Tip

When a question asks about plan differentiation, map each option to the correct product domain. Copilot Business commonly emphasizes security and compliance, while items about CI runners or core Git behavior usually belong to other parts of the platform.

Question 5

Your engineering group is participating in an early access evaluation of GitHub Copilot Chat and you have been asked to send thorough feedback that covers how the assistant uses context, the quality of the conversation flow, and any problems you encounter during everyday coding. Which channel should you use to share detailed feedback so it reaches the Copilot Chat team?

• [*] B. Use the feedback control in your supported IDE to submit Copilot Chat specific feedback

The correct option is Use the feedback control in your supported IDE to submit Copilot Chat specific feedback. This is the official path for detailed product input during early access and it routes your comments and optional diagnostics directly to the Copilot Chat team so they can evaluate context handling, conversation quality, and any issues you encounter during daily coding.

Submitting feedback from within the IDE includes relevant environment details such as the IDE version and extension version when you allow it. This makes your report actionable so the team can reproduce problems and correlate them with telemetry where permitted. It also ensures your input is categorized as Copilot Chat specific and reaches the engineers responsible for the feature.

The GitHub Community Discussions for Copilot are helpful for peer conversation and tips, however they are not a reliable channel for structured early access feedback that needs to be tracked and triaged by the product team.

The Email GitHub Support with your account details and a thorough description route is intended for account or access issues and general support needs. Support can help with troubleshooting, yet product feedback for Copilot Chat should be sent through the in editor feedback channel so it reaches the engineering team directly.

The Open an issue in the official GitHub Copilot repository option is not appropriate because Copilot and Copilot Chat do not use a public repository issue tracker for product bugs or feedback, therefore issues opened this way will not reliably reach the team.

Exam Tip

When an option points to an in product feedback control or another official path, prefer it for product feedback questions because it carries useful context and routes to the right team.

Question 6

What is the best way to ensure GitHub Copilot suggestions are accessible for a developer who uses a screen reader?

• [*] B. Use an IDE with robust screen reader support and verified Copilot compatibility

The correct option is Use an IDE with robust screen reader support and verified Copilot compatibility.

This approach ensures the coding environment exposes Copilot suggestions and commands to assistive technologies through established accessibility APIs and reliable keyboard navigation. Mature editors such as Visual Studio Code, Visual Studio, and JetBrains IDEs document accessibility features and have official Copilot integrations which means screen reader users can consistently review, accept, and manage suggestions.

Use GitHub.dev in the browser is not the best choice because the web editor offers a lighter feature set and its Copilot behavior and screen reader support are more limited and can vary by browser which makes it less dependable for fully accessible suggestion review and control.

Turn off Copilot removes AI suggestions entirely and does not address the need for accessible suggestions.

Exam Tip

Scan options for explicit mention of screen reader support and supported IDEs. Prefer answers that tie the feature to an environment with documented accessibility rather than disabling the feature or moving to a generic platform.

Question 7

Your team at example.com uses GitHub Copilot in a monorepo and notices that its completions frequently mirror widely used idioms from well known libraries and frameworks. How can the dominance of “most seen” examples in the training data shape the suggestions and what kinds of problems could that create?

• [*] C. Copilot tends to favor patterns it has encountered most often, which can be a poor fit for unusual or innovative code needs

The correct option is Copilot tends to favor patterns it has encountered most often, which can be a poor fit for unusual or innovative code needs.

This is how probabilistic code models behave because they learn from large corpora and generate what is most likely given the prompt and context. In a monorepo that mixes many services and frameworks this tendency pulls suggestions toward familiar idioms and widely used libraries. That can overshadow project specific abstractions or architectural choices and it can nudge the team toward the status quo rather than the approach that best serves the unique requirement.

This bias can create several problems. It can produce code that looks reasonable yet misaligns with your performance constraints or security posture or style guidelines. It can encourage common but suboptimal patterns and it can propagate outdated approaches that were prevalent in the training data. It can also reduce exploration of innovative designs because the assistant keeps steering back to the most seen solutions.

Using Cloud Code enforces project conventions so Copilot suggestions are immune to training data bias is incorrect because enforcing conventions or templates does not change how the model was trained. Such tools can shape structure and configuration in your project yet the assistant can still favor widely seen examples over specialized needs.

The model always produces novel solutions and does not rely on prior examples is incorrect because the assistant is trained on existing code and predicts likely continuations from that distribution. It often recombines known patterns and novelty is not guaranteed.

Frequently observed patterns guarantee the most optimal and efficient code for any context is incorrect because popularity does not equal optimality. The best solution depends on constraints such as runtime, memory, security, and maintainability and the most common pattern can be a poor choice in a specific context.

Exam Tip

Look for absolute words like always, guarantee, or claims of immunity because they often signal an incorrect choice. Favor answers that acknowledge how learned distributions bias suggestions and evaluate fit for the given context.

Question 8

At BrightWave Studios you oversee 72 engineers who use Copilot Business each day. Executives want a monthly summary that shows how frequently Copilot is used with a breakdown by programming language and by code editor so they can evaluate return on investment. What approach should you take to generate this report?

• [*] C. Use the GitHub REST API to pull organization Copilot usage metrics

The correct option is Use the GitHub REST API to pull organization Copilot usage metrics.

This approach gives you official organization wide Copilot usage data with dimensions for programming language and code editor. You can request a date range and aggregate results into a monthly summary so executives can see adoption and return on investment. It can be scripted and scheduled which makes it repeatable and accurate for all 72 engineers.

The GitHub REST API to pull organization Copilot usage metrics supports filtering and grouping so you can produce breakdowns by editor and by language without manual effort. You can export the results into a spreadsheet or a business intelligence tool and you can automate delivery each month.

Enable Dependabot alerts and infer Copilot usage from the security dashboard is incorrect because Dependabot focuses on dependency vulnerabilities and security insights and it does not track Copilot activity or provide language or editor breakdowns.

Ask each developer to export editor logs and manually consolidate usage is incorrect because it is manual and inconsistent and it would be error prone and would not reliably normalize usage across different editors or give a trustworthy organization wide view.

Cloud Monitoring is incorrect because it monitors cloud resources and services rather than GitHub Copilot activity and it does not integrate to provide Copilot usage metrics by language or editor.

Exam Tip

When a question asks for organization wide metrics with specific breakdowns look for a first party API that exposes those dimensions and avoid answers that rely on inference or manual collection.

Question 9

A fintech team at example.com is building an analytics service that handles customer identifiers and other personally identifiable information. As the team enables GitHub Copilot, they want to make sure sensitive files and comments are not included in Copilot’s context for code suggestions because Copilot reads local content to generate prompts. What is the best way to keep this information out of Copilot’s context?

• [*] B. Configure a .copilotignore file to prevent sensitive files and directories from being sent as context to GitHub Copilot

The correct option is Configure a .copilotignore file to prevent sensitive files and directories from being sent as context to GitHub Copilot.

This approach explicitly excludes listed files and folders from being read for prompt context which keeps customer identifiers and other sensitive content out of Copilot suggestions. It is proactive and repo scoped, which means the team can version and review the ignore rules and ensure consistent protection across all contributors and supported Copilot experiences.

Temporarily disable GitHub Copilot while working on sections that contain confidential data is manual and error prone and it does not scale for a team. It also provides no guard once Copilot is re enabled or for other collaborators, so it is not the best way to systematically prevent sensitive context from being read.

Use GitHub Advanced Security secret scanning to detect exposure of sensitive tokens in the repository is a valuable detection control for exposed credentials after they are committed, but it does not influence what Copilot reads locally for context. It does not prevent sensitive files or comments from being included in prompts.

Replace real identifiers with masked sample values in comments and sample data before using Copilot relies on manual redaction and is easy to miss, and it also reduces the usefulness of examples. It does not guarantee that other sensitive files will be excluded from Copilot context, so it is not a reliable preventive control.

Exam Tip

When a question asks how to keep sensitive data out of a tool’s context, favor controls that prevent the tool from reading or sending the data in the first place rather than after the fact detection or manual workflows.

Question 10

A development team at CanyonWorks Analytics uses GitHub Copilot to speed up delivery of a new API that will run on Cloud Run and connect to Cloud SQL. They intend to move this service to production within 45 days. What is a responsible practice when integrating Copilot generated code into the production service?

• [*] D. Perform code review and run security and license checks on Copilot generated code before promotion to production

The correct option is Perform code review and run security and license checks on Copilot generated code before promotion to production. This ensures the generated code meets quality, security, and compliance requirements before it reaches your Cloud Run service and connects to Cloud SQL.

This practice adds a human review step that can catch logic flaws, insecure patterns, secrets, and improper data handling that compilation and unit tests do not detect. Pairing review with automated scanning such as static analysis, dependency vulnerability checks, license compliance checks, and secret scanning establishes a reliable gate in continuous integration so only vetted changes are promoted to production within the planned timeline.

Adopt Copilot suggestions without changes if the code compiles and unit tests succeed is incorrect because compilation and unit tests do not validate security posture, licensing obligations, or the presence of hardcoded secrets. Relying on those signals alone invites vulnerabilities and compliance issues into production.

Turn off telemetry and feedback collection to address privacy concerns is incorrect because disabling telemetry does not improve the safety or compliance of the generated code. Responsible adoption focuses on review and scanning to manage risk rather than turning off data collection.

Depend on Cloud Armor and VPC Service Controls to offset any insecure code patterns is incorrect because these are perimeter and data exfiltration controls. They help with request filtering and service isolation but they do not remediate insecure application logic, injection flaws, or license violations introduced by the code itself.

Exam Tip

When a scenario mentions generative code headed to production, favor options that add human review and security and license gates in the pipeline over options that rely on perimeter controls or simple test success.

Question 11

Priya is a backend engineer at scrumtuous.com who uses GitHub Copilot Individual in IntelliJ IDEA, and she wants to understand how suggestions appear while typing and whether she can request alternative completions on demand. Which statement correctly describes how GitHub Copilot Individual behaves inside a supported IDE?

• [*] C. GitHub Copilot offers inline completions as you type and lets you browse alternate suggestions using keyboard shortcuts

The correct answer is GitHub Copilot offers inline completions as you type and lets you browse alternate suggestions using keyboard shortcuts.

This choice matches how Copilot behaves in JetBrains IDEs like IntelliJ IDEA. As you type, Copilot proposes inline suggestions that appear as faint text inside the editor and you can accept them or keep typing to refine them. You can also request additional completions and cycle through alternatives using keyboard shortcuts, for example Alt with bracket keys on Windows and Linux or Option with bracket keys on macOS.

GitHub Copilot only works when the project is stored in a GitHub repository and cannot assist in local folders is incorrect because Copilot runs inside supported editors and can suggest code for local projects that are not hosted on GitHub.

GitHub Copilot never provides inline suggestions automatically and only activates with a specific key press is incorrect because Copilot can surface inline suggestions proactively as you type and it also supports shortcut based triggering when you want to ask for alternatives or more suggestions.

GitHub Copilot automatically creates full applications without developer prompts and does not need review is incorrect because Copilot proposes code snippets and patterns yet it does not autonomously build complete applications and its output requires developer review and testing.

Exam Tip

When options use absolute words like never or only be skeptical and look for statements that reflect how the tool behaves in real editors such as inline suggestions that appear as you type and the ability to cycle alternatives with shortcuts.

Question 12

How does GitHub Copilot process editor code snippets to maintain confidentiality and avoid retaining them?

• [*] C. GitHub Copilot sends minimal editor context to the service for a real time suggestion and it discards the snippet after responding

The correct option is GitHub Copilot sends minimal editor context to the service for a real time suggestion and it discards the snippet after responding.

This is right because Copilot transmits only the small portion of code and relevant signals needed to create a suggestion. The service uses that context to generate a completion in real time and then it does not retain the snippet. For business and enterprise offerings, prompts and suggestions are not stored or used to train models which supports confidentiality by design.

GitHub Copilot runs only within the IDE so no code ever leaves the machine is incorrect because Copilot relies on a cloud service to produce completions. It must send limited editor context over the network to work, therefore some code leaves the machine for the request.

GitHub Copilot forwards snippets to Azure OpenAI and keeps anonymized prompts in logs for 30 days is incorrect because the confidentiality posture for Copilot Business and Enterprise is that prompts and suggestions are not retained and are not used to train models. The service processes the request and discards the snippet rather than keeping anonymized prompt logs for a fixed retention window.

GitHub Copilot encrypts snippets in transit and at rest and it stores them indefinitely for future training is incorrect because while encryption is used, indefinite storage and training on customer prompts or suggestions do not occur in Copilot Business and Enterprise.

Exam Tip

Map statements about data handling to vendor language. Look for phrases like minimal context and no retention and challenge absolutes that claim everything stays local or that data is stored indefinitely.

Question 13

A developer at mcnz.com is working in Visual Studio Code with GitHub Copilot enabled and needs to ask for an explanation of a complex function and get context about the surrounding code without leaving the editor, which capability should they use?

• [*] C. GitHub Copilot Chat

The correct option is GitHub Copilot Chat because it lets you ask for an explanation of a complex function and get context about the surrounding code without leaving Visual Studio Code.

This feature gives you a chat panel and inline chat inside the editor so you can ask questions about selected code, request explanations, and get summaries while it uses your workspace to ground its answers. You can stay in the editor and have it reason over open files and project symbols to provide relevant context.

The option GitHub Copilot Inline Suggestions focuses on code completions as you type and it does not offer an interactive conversation that explains code or pulls in broader project context.

The option GitHub Copilot CLI is designed for the terminal and helps with shell commands and Git tasks and it is not the in editor chat experience for discussing your code.

The option GitHub Copilot Multiple Suggestions shows alternative completion candidates and it is still a suggestion workflow rather than a chat that explains complex functions with workspace context.

Exam Tip

Scan the prompt for verbs like ask and explain and for phrases like without leaving the editor. These point to the chat capability rather than code suggestions or the terminal.

Question 14

At mcnz.com a developer is using GitHub Copilot to draft a Python function that returns the largest number in a list. Which prompt wording would most likely lead Copilot to produce correct and concise code?

• [*] C. Generate a Python function that returns the maximum number from a list input

The correct option is Generate a Python function that returns the maximum number from a list input. This wording gives Copilot a clear task, the programming language, the behavior to implement, and the input type, which guides it to produce a focused and concise solution.

This prompt specifies Python, asks for a function, and defines both the input and the expected result. Copilot tends to produce the most accurate code when the request clearly states the goal and constraints, so this phrasing reduces ambiguity and encourages a minimal correct implementation that returns the largest element from the list.

Cloud Functions is unrelated to the task and does not describe a Python function or what it should return, so it would not guide Copilot to the needed code.

Compute the average of the numbers instructs Copilot to solve a different problem, since averaging is not the same as finding the maximum value.

Compare things in Python is vague and lacks details about language context, input type, and desired output, so it would likely yield generic or unfocused suggestions rather than a concise function that returns the largest number.

Exam Tip

When prompting Copilot, state the language, the exact goal, the input type, and the expected output. Clear constraints lead to concise and correct code.

Question 15

Your engineering team at NovaStream uses WebStorm and GoLand alongside Visual Studio Code for daily development. You plan to roll out GitHub Copilot in a consistent way across both environments. What should you do in the JetBrains IDEs to make sure Copilot integrates smoothly into the team’s workflow?

• [*] C. Install the GitHub Copilot plugin from JetBrains Marketplace then sign in with your GitHub account and have each developer enable it for their IDE and project

The correct option is Install the GitHub Copilot plugin from JetBrains Marketplace then sign in with your GitHub account and have each developer enable it for their IDE and project.

This approach installs Copilot natively in WebStorm and GoLand and it aligns the experience with Visual Studio Code. It uses the official JetBrains Marketplace distribution and a GitHub sign in so each developer activates their license and settings. Enabling it per IDE and per project ensures consistent availability and lets you apply organization policies and project level controls.

Rely on JetBrains’ built in code completion features instead of GitHub Copilot is wrong because JetBrains completion does not provide Copilot cloud powered suggestions or chat and it does not integrate with GitHub policies or telemetry.

Install the Cloud Code plugin for JetBrains and configure it for the project to provide AI assisted development is wrong because Cloud Code targets Google Cloud workflows and Kubernetes and it is unrelated to GitHub Copilot.

Copy suggestions from Visual Studio Code into JetBrains editors because Copilot does not run natively in JetBrains is wrong because Copilot runs natively in JetBrains IDEs through the official plugin so manual copying is unnecessary and would disrupt the workflow.

Exam Tip

Prefer the official integration path. Look for the vendor marketplace plugin, a required account sign in, and per IDE or project enablement. Options that avoid installation or suggest manual copying are usually incorrect.

Question 16

At scrumtuous.com you are developing a TypeScript service in Visual Studio Code with “GitHub Copilot” enabled and you want to understand how suggestions are generated and whether any of your code is retained by the service. Which description best matches the “Copilot” data lifecycle for your prompts and completions?

• [*] C. Your editor content is processed only in memory to produce suggestions and the Copilot model runs on GitHub servers trained on public repositories and your code is neither stored nor logged

The correct option is Your editor content is processed only in memory to produce suggestions and the Copilot model runs on GitHub servers trained on public repositories and your code is neither stored nor logged.

This description matches how GitHub Copilot generates completions because your editor sends relevant context to a GitHub operated inference service that returns suggestions and the context is handled transiently to produce the completion. Copilot is trained on public code and GitHub explains that your private prompts and completions are not used to train the model and that the service focuses on in-memory processing of your context to produce suggestions.

All code analysis and suggestion generation run only on your laptop and no data is ever sent to any remote service is wrong because Copilot relies on a cloud hosted model endpoint and your editor must contact that service to get completions so it is not a fully local system.

Snippets from your editor are regularly captured and stored to continuously retrain the Copilot model is wrong because GitHub does not use your code to train the Copilot model and it does not continuously harvest your editor snippets for training.

GitHub Copilot uses Vertex AI Codey in your Google Cloud project to host the model and prompts are stored in your project for future fine tuning is wrong because Copilot is operated by GitHub and it does not run on your Google Cloud project and it does not use Google Vertex AI Codey or store prompts in your project for fine tuning.

Exam Tip

When choices discuss privacy and training, look for whether prompts are processed transiently and whether they are used to train the model. Claims of local only processing usually do not fit Copilot, and statements that prompts are stored and used for training are red flags. Pay attention to the retention scope and who hosts the model.

Question 17

An engineer at mcnz.com maintains a Python endpoint running on Cloud Run that relies on a naive recursive Fibonacci function which is correct but becomes very slow when n grows. You want to use GitHub Copilot to surface performance issues and to propose code changes while also improving the test suite. What should you ask Copilot to do to optimize the implementation and generate more effective tests?

• [*] C. Request that Copilot refactor the function using memoization or an iterative dynamic programming method and produce tests that cover larger input sizes

The correct option is Request that Copilot refactor the function using memoization or an iterative dynamic programming method and produce tests that cover larger input sizes.

Naive recursive Fibonacci has exponential time complexity and quickly becomes unusable as n increases. Asking Copilot to apply memoization or to rewrite the function iteratively with dynamic programming reduces the complexity to linear time and it eliminates redundant work. Pairing that change with stronger tests that exercise larger inputs validates both correctness and performance and it helps prevent regressions.

Copilot can propose concrete code changes that cache results or build the sequence iteratively and it can generate unit tests that check boundary cases, typical values, and higher n that would have been impractical before. This approach focuses the assistant on optimization and verification together which aligns with the goal of surfacing performance issues and proposing code changes while improving the test suite.

The option Cloud Profiler is not appropriate because the question asks you to use GitHub Copilot to refactor code and expand tests. Cloud Profiler is a Google Cloud performance profiling service and it does not ask Copilot to change the implementation or generate tests.

Tell Copilot to create stress tests for extremely large values like n equals 2500 and then ignore any refactoring suggestions it provides is counterproductive. Stressing an exponential algorithm with such large inputs will likely time out or crash and ignoring refactoring defeats the main objective of improving performance and code quality.

Have Copilot rewrite the routine with deeper recursive branching and write tests only around that new recursion in the hope that performance will improve automatically is incorrect because deeper recursion increases work and risk of stack overflows. Tests limited to the new recursion do not verify performance gains or broader correctness.

Exam Tip

Scan options for explicit requests to both optimize the algorithm and strengthen tests. Look for concrete techniques such as memoization or iterative dynamic programming and prefer prompts that ask Copilot to change code and validate results with realistic input ranges.

Question 18

Which GitHub Copilot plan provides enterprise level governance, security, and compliance for teams that work in both public and private repositories?

• [*] C. Copilot Enterprise

The correct option is Copilot Enterprise.

This plan is built for organizations that need strong governance along with security and compliance. It provides centralized policy management and enterprise controls with auditability and it supports teams working across both public and private repositories, which aligns with the scenario in the question.

Copilot Business is suited for teams that need seat management and some policy controls, yet it does not include the full enterprise governance and compliance capabilities that the scenario requires.

GitHub Advanced Security is a separate security product that provides code scanning, secret scanning and supply chain security features. It is not a Copilot plan and it does not address the Copilot subscription needs described in the question.

Copilot Individual is a single user subscription and it lacks organizational policy management and enterprise compliance features, so it does not meet the requirement.

Exam Tip

Map the requirement keywords to the plan names. When you see needs such as enterprise governance and compliance for teams across public and private repositories, that points to the enterprise tier rather than individual or team plans.

Question 19

You use an AI coding assistant in your IDE to generate Python and you want it to produce a PyTorch function that trains a neural network with higher accuracy. You have two short snippets from a previous project that show how you load data and define the model, and you plan to paste them before your request so you can apply few-shot prompting. How should you word your prompt so the assistant follows your examples and completes the training function?

• [*] C. Implement a PyTorch training function and use the two example snippets below that show data loading and model setup as guidance

The correct option is Implement a PyTorch training function and use the two example snippets below that show data loading and model setup as guidance.

This phrasing tells the assistant to write the training function in PyTorch while explicitly using your two snippets as guiding examples. It enables few-shot prompting because the examples precede the request and the instruction clearly asks the model to follow them. This reduces drift from your data pipeline and model setup and increases the chance that the generated training loop matches your conventions and improves accuracy.

Write PyTorch training code and I will provide examples later is wrong because it delays the examples and does not instruct the assistant to follow them. Without immediate context the model is more likely to ignore the intended patterns.

Use Vertex AI to build and train the model instead of writing PyTorch code is wrong because it changes the tool and platform and it does not satisfy the requirement to produce PyTorch code in your IDE.

Create a function that uses PyTorch to train a neural network is wrong because it is too generic and it does not direct the assistant to follow your two example snippets, which is the key to effective few-shot prompting.

Exam Tip

Place your examples before the request and tell the assistant to follow them. Include concrete instructions such as explicitly reference the two snippets as guidance and add success criteria like metrics or early stopping so the model knows what to optimize.

Question 20

You are the platform owner at a media analytics startup that deployed GitHub Copilot for Business. Engineers are concerned that Copilot could surface snippets from confidential repositories and leadership wants to ensure private code never contributes to training. You intend to update the Copilot editor configuration so both suggestions and training exclude private code. Which configuration should you apply?

• [*] C. Configure the “excludePatterns” rule in the Copilot editor config to ignore selected private repositories or directories during completions

The correct configuration is Configure the “excludePatterns” rule in the Copilot editor config to ignore selected private repositories or directories during completions.

This configuration ensures that the editor does not surface completions from specified private paths which addresses the engineers’ concern about suggestions. With Copilot for Business, prompts and code snippets from your private repositories are not used for training by default, so when you apply this editor rule for suggestions you also meet the leadership requirement about training because the plan’s privacy guarantees cover that aspect.

Cloud DLP is unrelated to GitHub or Copilot editor behavior because it is a Google Cloud data protection service and it cannot configure Copilot suggestions or training.

Enable “useBusinessRules” to true so organization privacy controls filter out internal code in suggestions is not a real Copilot editor setting and organization privacy is managed through documented Copilot settings such as content exclusions rather than a key with that name.

Turn on “corporateDataProtection” so proprietary code is safeguarded by enterprise privacy settings is not a valid Copilot configuration and it does not exist in the editor or organization settings.

Set “trainingData” to false in the Copilot editor configuration so private code is not used for model learning is incorrect because there is no such editor key. Copilot for Business already prevents your private code and prompts from being used to train the model, and training policies are not controlled from the editor.

Exam Tip

Trace where the control must live. If the prompt mentions editor configuration then look for settings that affect completions in the IDE, and if the goal is about training then confirm whether the plan’s privacy policy already covers it rather than expecting an editor switch.

Cameron McKenzie is an AWS Certified AI Practitioner, Machine Learning Engineer, Solutions Architect and author of many popular books in the software development and Cloud Computing space. His growing YouTube channel training devs in Java, Spring, AI and ML has well over 30,000 subscribers.