20 Tough GitHub Copilot Certification Exam Questions and Answers

The engineering director at Riverstone Analytics manages several private repositories and is worried that their proprietary code could be incorporated into future GitHub Copilot model training. The company uses GitHub Copilot for Business in its GitHub organization and needs a configuration that guarantees their content is excluded from model training by GitHub. What should they configure?

• ❏ A. Disable GitHub Copilot across the organization

• ❏ B. Limit repository visibility to internal members only

• ❏ C. Enable Content Exclusions in GitHub Copilot for Business at the organization level

• ❏ D. Enable Google Cloud Data Loss Prevention for the source code repositories

At HarborTrust Finance your team is replacing a 25 year old COBOL claims engine with Python services built on a cloud native stack. You plan to use GitHub Copilot during the rewrite to accelerate development while avoiding regressions. How should you apply GitHub Copilot to best support the migration and still maintain correctness?

• ❏ A. Let GitHub Copilot regenerate every business rule in Python and assume behavior will match the original

• ❏ B. Use GitHub Copilot to propose Python snippets for specific COBOL routines and validate them with thorough tests and peer review

• ❏ C. Duet AI in Google Cloud

• ❏ D. Rely on GitHub Copilot to fully translate the COBOL codebase into Python with no developer oversight

You contract for a small digital studio and you plan to subscribe to GitHub Copilot Individual to accelerate development in several programming languages. Before purchasing, you want to verify a principal capability that is included with the Copilot Individual plan. Which feature is provided by GitHub Copilot Individual?

• ❏ A. Unlimited GitHub Actions minutes and artifact storage included with the Copilot Individual plan

• ❏ B. Enterprise security and compliance features with automated code scanning and integration with GitHub’s advisory database

• ❏ C. AI powered code completions across many languages in supported editors such as VS Code JetBrains IDEs and Neovim with context aware inline suggestions as you type

• ❏ D. Cloud Build

At a startup building a dashboard for scrumtuous.com you work in an editor on a project that uses HTML JavaScript and Python. After spending about 120 lines editing a Python script you switch to a JavaScript file to add live interface updates. Copilot begins surfacing completions that mirror the earlier Python changes rather than the JavaScript patterns in the file. What should you do so Copilot focuses its suggestions on the current JavaScript context?

• ❏ A. Trust Copilot to detect the language automatically and continue without adding any contextual hints

• ❏ B. Move the JavaScript work into a fresh workspace so the Python code does not influence suggestions

• ❏ C. Add clear JavaScript context by writing a brief comment and some scaffolded code in the active file so Copilot infers the language and task

• ❏ D. Close every Python tab to force a context reset before you continue coding in JavaScript

What is the best approach to mitigate bias in GitHub Copilot suggestions while continuing to use AI assisted coding in a shared repository?

• ❏ A. Disable Copilot for this repository

• ❏ B. Keep using Copilot and fix biased terms as you notice them

• ❏ C. Adopt an inclusion review workflow for Copilot output and require neutral language with team training

• ❏ D. Enable GitHub Advanced Security and rely on secret scanning as the main mitigation

A product team at scrumtuous.com is choosing between GitHub Copilot for Individuals and the Business or Enterprise plans. They require centralized identity controls to meet company policy. Which capability exists only in the Business and Enterprise offerings and is not included with the Individuals plan?

• ❏ A. Pull request insights and suggestions on GitHub

• ❏ B. Single Sign-On integration with an identity provider

• ❏ C. In-editor code completions and chat

• ❏ D. Complimentary access for verified open source maintainers

You want to understand how an AI coding assistant in your IDE turns your current code context into a suggestion. Which description best captures the flow from your editor to the generated completion?

• ❏ A. Your editor streams your repository into Vertex AI so a managed model retrains continuously before sending back a suggestion

• ❏ B. The tool crawls public repos in the background and learns from them in real time to tailor suggestions for your current file

• ❏ C. The IDE sends a compact prompt that includes nearby code to a vendor service where a hosted large language model returns a completion which the IDE then displays

• ❏ D. The assistant runs fully inside the IDE and retrains on the entire project each time you request a completion

A development team at scrumtuous.com wants to make pull request reviews faster and more consistent by using GitHub Copilot Enterprise. Which capability in Copilot Enterprise directly assists reviewers by summarizing what changed and which files were modified in a pull request?

• ❏ A. It blocks any pull request that violates the team’s style rules by automatically rejecting it

• ❏ B. Cloud Code

• ❏ C. It produces AI generated summaries for pull requests that highlight key changes and the files affected

• ❏ D. It delivers detailed metrics about each engineer’s performance during code reviews

A platform engineering group at mcnz.com administers a GitHub Enterprise organization and needs to control GitHub Copilot behavior consistently across all projects. Which organization level configuration can they apply to manage how Copilot suggestions are provided?

• ❏ A. Prevent Copilot from scanning public GitHub repositories

• ❏ B. Decide if Copilot Chat is permitted inside terminal sessions

• ❏ C. Enable or disable Copilot code suggestions by repository, file type, or team

• ❏ D. Use Google Cloud Organization Policy to disable AI assisted coding across the enterprise

When using Copilot Individual with the default settings, how are code context and interaction data handled by default?

• ❏ A. Only aggregate telemetry without code snippets is sent

• ❏ B. GitHub may receive context and interactions for telemetry and improvement unless opted out

• ❏ C. All processing is local and nothing is sent

• ❏ D. Enterprise policy controls all data handling

My GitHub Practice Exams are on Udemy. My free copilot practice tests are on certificationexams.pro

You are building a Cloud Run service for Globetrotter Labs that calls a third party weather endpoint at api.example.com, and the provider’s guide is missing important details. You must write a function that issues the request and parses the JSON while keeping the code efficient, and you also need to handle up to 20 second timeouts and occasional invalid payloads. In this situation how can GitHub Copilot increase your productivity? (Choose 2)

• ❏ A. Apigee will auto generate complete client libraries and test suites for any external API so you do not need to write client code

• ❏ B. Copilot can draft unit tests for the integration that include cases like a 20 second timeout or malformed JSON from the provider

• ❏ C. Copilot removes the need to read the provider documentation or call the API yourself

• ❏ D. Copilot can propose an implementation for the HTTP call and response parsing based on the surrounding code and patterns in your repository

• ❏ E. Copilot will perfectly infer the provider’s schema and produce exact request and parsing code for every endpoint

A product team at Northwind Health Tech codes in Google Cloud Workstations and uses GitHub Copilot while working with application files that include customer PII from example.com. The team wants to ensure that none of this sensitive content is sent to Copilot when suggestions are generated. What should the team do?

• ❏ A. Use Google Cloud Sensitive Data Protection in Cloud DLP to scan commits and block PII before merges

• ❏ B. Rely on GitHub Copilot default privacy settings to automatically filter out sensitive values from context

• ❏ C. Disable GitHub Copilot for the specific repository folders or files that contain regulated data

• ❏ D. Insert a comment at the top of the file asking Copilot not to read the following section

BrightWave Labs wants to boost everyday use of GitHub Copilot across its engineering group and the lead is looking for an approach that is primarily centered on language and communication rather than tooling changes or formal training, so which approach would most closely align with this goal?

• ❏ A. Publish a detailed handbook with prompt guidelines and team-specific best practices

• ❏ B. Host recurring peer showcases where developers share successful prompts and outcomes in the team’s chat and meetings

• ❏ C. Cloud Build

• ❏ D. Integrate Copilot into the standard IDE images and project workflows so it is available by default

A digital publisher at example.com recently enabled GitHub Copilot Business and wants to demonstrate compliance with its access management policy by using platform telemetry. Which statement best describes a security advantage of Copilot Business audit logs?

• ❏ A. Copilot Business audit logs reveal the reasoning behind each AI suggestion so reviewers can understand why a completion was generated

• ❏ B. Copilot Business audit logs automatically block suspicious sign-ins and enforce multi factor authentication without any additional security tooling

• ❏ C. Copilot Business audit logs record user and administrator activity which helps teams spot unauthorized access and perform access reviews

• ❏ D. Copilot Business audit logs provide detailed error diagnostics for code suggestions to simplify organization wide troubleshooting

On GitHub.com, which setting prevents Copilot from suggesting code that matches public repositories while still allowing AI generated suggestions?

• ❏ A. Enable GitHub Advanced Security

• ❏ B. Enable Cloud DLP in Google Cloud

• ❏ C. Enable Copilot filter that blocks public code matches

• ❏ D. Set organization repository exclusions in Copilot

Your team at Northpeak Labs maintains a large monorepo with many intertwined modules and engineers notice that GitHub Copilot Chat slows down when too many files are involved. What practice will help Copilot Chat respond faster while you work on a focused task?

• ❏ A. Paste larger code blocks into each prompt so Copilot Chat sees entire functions and classes

• ❏ B. Redesign the codebase to remove cross module dependencies so each file stands alone for Copilot Chat

• ❏ C. Keep only the files you are actively working on open and close unrelated files to shrink the context Copilot Chat processes

• ❏ D. Move the repository to Cloud Source Repositories to reduce latency for Copilot Chat

Lakeside Robotics uses GitHub Enterprise Cloud with Copilot and needs to track access and configuration activity for security and compliance reviews. How do organization audit logs help administrators manage Copilot in this situation?

• ❏ A. Organization audit logs push immediate alerts about code quality issues in Copilot suggestions

• ❏ B. Organization audit logs capture the full text of Copilot outputs for later human review

• ❏ C. Organization audit logs show which users enabled or accessed Copilot and record administrative changes such as seat or subscription updates

• ❏ D. Organization audit logs track the count of Copilot prompts per user so administrators can enforce per user rate limits

At scrumtuous.com you are building a new marketplace service with GitHub Copilot and you need a robust routine that computes shipping fees using package weight travel distance and delivery tier. A short prompt like “create a function that computes shipping fees” produced simplistic code. You want to refine your prompt so the generated function handles edge cases and returns a clear object with breakdowns and totals. Which prompting approach will most likely yield a detailed and reliable shipping fee calculation?

• ❏ A. Paste a partially written function and rely on Copilot to fill the gaps based on common e commerce patterns

• ❏ B. Use Vertex AI Codey to generate the function instead of improving the Copilot prompt

• ❏ C. Write a structured prompt that enumerates weight distance and service level with sample inputs unit assumptions and the exact output shape

• ❏ D. Keep the request broad and trust Copilot to infer sophisticated pricing logic from prior training

You are building services at Coastal Ridge Labs and you use GitHub Copilot in Visual Studio Code to speed up development, and you want to invoke suggestions quickly in the editor and in the terminal depending on what you are doing, so which actions will cause Copilot to propose code suggestions? (Choose 2)

• ❏ A. Press Ctrl+Space on a blank line in a Python module to trigger a suggestion

• ❏ B. Write a natural language comment in a JavaScript file then pause and press Tab to accept the inline suggestion that appears

• ❏ C. Let a runtime error occur in a Go program and wait for Copilot to automatically insert a fix

• ❏ D. Run gh copilot suggest in the terminal to request a code suggestion from the GitHub CLI

Which feature is available in GitHub Copilot Enterprise but not in GitHub Copilot Business?

• ❏ A. Private fine tuning on private repositories

• ❏ B. Copilot Chat grounded by company knowledge

• ❏ C. On premises deployment

• ❏ D. Full offline mode

The correct option is Enable Content Exclusions in GitHub Copilot for Business at the organization level.

This setting provides an explicit policy that tells GitHub not to use the organization’s selected content for training future GitHub Copilot models. It is designed for GitHub Copilot for Business and is configured by org admins so it applies consistently across the repositories you choose. It addresses the director’s concern directly because it creates a clear exclusion that is enforced by GitHub rather than relying on indirect controls.

Disable GitHub Copilot across the organization is not the right choice because it only stops users from accessing Copilot features and it does not establish a training exclusion policy for existing or future repository content. It also removes the productivity benefits of Copilot without providing the required guarantee.

Limit repository visibility to internal members only is incorrect because visibility governs who can access the repositories and it does not control whether content can be used for model training. Only a Copilot content exclusion policy can guarantee the required outcome.

Enable Google Cloud Data Loss Prevention for the source code repositories is unrelated to GitHub Copilot policy controls and it cannot govern how GitHub trains its models. It does not provide any training exclusion for GitHub services.

The correct option is Use GitHub Copilot to propose Python snippets for specific COBOL routines and validate them with thorough tests and peer review.

This approach treats Copilot as an assistant that suggests code while engineers retain ownership of design and correctness. By focusing on specific routines you keep changes small and understandable. You then protect behavior with unit tests, integration tests, and regression tests, and you use peer review to catch logic errors and ensure maintainability. This process fits a high risk migration from a long lived COBOL system because it couples speed with strong safeguards.

You can capture the intended behavior of critical business rules in tests before you replace them. Then you invite Copilot to propose Python implementations for those targeted routines. You run the full test suite in continuous integration to confirm parity and you require code review to verify edge cases and data handling. This workflow accelerates the rewrite while maintaining correctness.

Let GitHub Copilot regenerate every business rule in Python and assume behavior will match the original is risky because it relies on assumption rather than evidence. A legacy engine often encodes many edge cases and Copilot suggestions are probabilistic, so you must verify behavior with tests and reviews rather than assume equivalence.

Duet AI in Google Cloud is a different product and does not answer how to apply GitHub Copilot for this migration. It is outside the scope of a GitHub focused solution and its branding has been superseded, which makes it less likely to appear on newer exams.

Rely on GitHub Copilot to fully translate the COBOL codebase into Python with no developer oversight removes essential human judgment and quality controls. An automated translation without tests and peer review can introduce subtle defects and compliance gaps, so it does not meet the requirement to maintain correctness.

The correct option is AI powered code completions across many languages in supported editors such as VS Code JetBrains IDEs and Neovim with context aware inline suggestions as you type.

This plan gives you intelligent suggestions while you type in popular editors. It supports many programming languages and uses the surrounding code and comments to generate context aware completions that help you write code faster with fewer manual keystrokes.

Unlimited GitHub Actions minutes and artifact storage included with the Copilot Individual plan is incorrect because Actions usage and storage are billed separately based on your GitHub plan and usage. The Copilot Individual subscription does not grant unlimited Actions minutes or artifact storage.

Enterprise security and compliance features with automated code scanning and integration with GitHub’s advisory database is incorrect because those capabilities are part of GitHub Advanced Security and enterprise offerings. Copilot Individual does not include enterprise grade code scanning or compliance features.

Cloud Build is incorrect because it is an unrelated Google Cloud service and is not a feature of GitHub Copilot Individual.

The correct option is Add clear JavaScript context by writing a brief comment and some scaffolded code in the active file so Copilot infers the language and task.

Copilot draws heavily on the active file and nearby code. When you add a short JavaScript comment and a small scaffold like a function signature or a stubbed event handler, you create strong signals about both the language and the intent. This approach quickly steers completions back to JavaScript patterns and keeps you moving without leaving the file.

Trust Copilot to detect the language automatically and continue without adding any contextual hints is not reliable when the current file lacks clear cues or when recent edits in another language are still influencing suggestions. Without explicit context, the model may keep echoing Python style changes.

Move the JavaScript work into a fresh workspace so the Python code does not influence suggestions adds overhead without addressing the real issue. Copilot primarily uses the active file and immediate context, so changing workspaces does not provide the strong in file signals that guide suggestions.

Close every Python tab to force a context reset before you continue coding in JavaScript does not give positive guidance about your intent. The editor state is less important than the cues inside the file you are editing, and adding clear JavaScript context is a faster and more effective fix.

The correct option is Adopt an inclusion review workflow for Copilot output and require neutral language with team training.

This approach preserves the advantages of AI assisted coding while adding clear guardrails to collaboration. You can add inclusive language guidelines to contributing documentation and code review templates, require pull request reviews that check generated code and comments for non inclusive or biased wording, and use automation with linters or checks to flag problematic terms. Team training aligns reviewers on the same standards so the practice is consistent and auditable.

You can enforce the workflow with branch protection and required reviews so nothing merges until it meets inclusion standards. This shifts mitigation to policy and review which scales across a shared repository rather than relying on individual vigilance.

Disable Copilot for this repository eliminates AI assistance and conflicts with the requirement to continue using AI assisted coding. It avoids the issue instead of mitigating it and sacrifices productivity benefits.

Keep using Copilot and fix biased terms as you notice them is reactive and inconsistent because it depends on individuals to catch issues case by case. It will miss incidents and it does not establish a shared or enforceable standard.

Enable GitHub Advanced Security and rely on secret scanning as the main mitigation targets exposure of credentials and similar sensitive artifacts rather than language bias. It is valuable for security yet it does not review wording or enforce inclusive terminology.

The correct option is Single Sign-On integration with an identity provider. This is the only capability that is part of the Business and Enterprise offerings and is not included with the Individuals plan.

Business and Enterprise plans are administered at the organization or enterprise level so they support centralized identity and access governance through an external identity provider. This lets administrators enforce sign in requirements across all licensed users and align access with company policy. The Individuals plan is tied to a personal GitHub account and cannot be governed by your company identity provider, so it does not provide centralized identity control.

Pull request insights and suggestions on GitHub does not address centralized identity needs and it is not the unique differentiator between the Individuals plan and both Business and Enterprise together. Some pull request experiences are scoped to specific higher tiers or are usability features rather than identity governance, so this is not the correct choice.

In-editor code completions and chat are included with the Individuals plan as well, so they are not exclusive to Business and Enterprise and they do not satisfy the requirement for centralized identity controls.

Complimentary access for verified open source maintainers is a benefit associated with individual eligibility and it is not a capability that distinguishes Business and Enterprise from Individuals.

The correct answer is The IDE sends a compact prompt that includes nearby code to a vendor service where a hosted large language model returns a completion which the IDE then displays.

This flow reflects how modern coding assistants operate. The IDE gathers relevant context such as the current file, nearby lines, language hints, and sometimes symbols or errors into a compact prompt. It sends that prompt to a hosted large language model through the vendor service. The service returns a completion that the IDE renders inline so you can accept or edit it. The model performs inference on your prompt rather than retraining on your project, which keeps responses fast and predictable.

Your editor streams your repository into Vertex AI so a managed model retrains continuously before sending back a suggestion is incorrect because interactive code completion does not retrain a model on your entire repository in real time. These assistants call a pretrained hosted model and provide only the necessary context in the prompt.

The tool crawls public repos in the background and learns from them in real time to tailor suggestions for your current file is incorrect because model training happens offline and not by live crawling during your editing session. Suggestions are generated from your prompt and the model’s existing parameters rather than continuous real time learning from public repositories.

The assistant runs fully inside the IDE and retrains on the entire project each time you request a completion is incorrect because full local retraining would be computationally impractical and unnecessary for quick completions. Typical assistants use a lightweight client in the IDE that communicates with a cloud model for inference.

The correct option is It produces AI generated summaries for pull requests that highlight key changes and the files affected. This directly helps reviewers by presenting a concise overview of what changed and which files were modified so they can focus their attention quickly.

Copilot Enterprise generates a natural language summary on the pull request that highlights the most important changes and calls out the impacted files. Reviewers can use this summary to understand scope and intent before reading the diffs, which speeds up reviews and makes them more consistent across the team.

It blocks any pull request that violates the team’s style rules by automatically rejecting it is not a Copilot Enterprise capability. Enforcing style and blocking merges is handled through branch protection rules, required status checks, or linters rather than Copilot.

Cloud Code is a different tool for cloud development workflows and it is not a Copilot Enterprise feature for pull request reviews.

It delivers detailed metrics about each engineer’s performance during code reviews is not provided by Copilot Enterprise and it would not align with GitHub’s emphasis on privacy and responsible use of AI.

The correct option is Enable or disable Copilot code suggestions by repository, file type, or team.

At the organization level, GitHub Enterprise administrators can define Copilot policies that control where and for whom code suggestions appear. These policies can target specific repositories, programming languages or file types, and teams so the platform engineering group can apply consistent rules across all projects.

The option Prevent Copilot from scanning public GitHub repositories is incorrect because Copilot does not scan public repositories on your behalf when generating suggestions. The relevant control allows you to block suggestions that match public code rather than disabling a scan of public repositories.

The option Decide if Copilot Chat is permitted inside terminal sessions is not the right control for this scenario because it focuses on access to a chat experience in the command line and it does not manage how code suggestions are delivered across repositories, languages, or teams.

The option Use Google Cloud Organization Policy to disable AI assisted coding across the enterprise is unrelated to GitHub administration and cannot enforce Copilot behavior inside a GitHub Enterprise organization.

The correct option is GitHub may receive context and interactions for telemetry and improvement unless opted out.

This is correct because with Copilot Individual using default settings, the service can collect prompts, code context, and interaction data to operate the service and to improve it. Telemetry and product improvement collection are enabled by default for individual users and you can turn them off in your account settings. This means snippets and related context from your sessions may be transmitted to GitHub for analysis and quality improvements unless you opt out.

Only aggregate telemetry without code snippets is sent is incorrect because Copilot Individual may send code snippets and relevant context by default and the collection is not limited to aggregate engagement data.

All processing is local and nothing is sent is incorrect because Copilot relies on cloud services to generate suggestions and therefore prompts and related context are sent to the service.

Enterprise policy controls all data handling is incorrect because an Individual plan is governed by the user’s own settings and not by enterprise or organization policy.

The correct options are Copilot can draft unit tests for the integration that include cases like a 20 second timeout or malformed JSON from the provider and Copilot can propose an implementation for the HTTP call and response parsing based on the surrounding code and patterns in your repository.

Copilot can draft unit tests for the integration that include cases like a 20 second timeout or malformed JSON from the provider because it can suggest test cases from your code and from natural language prompts. This helps you quickly cover edge cases like long request timeouts and invalid JSON while you still review and execute the tests to confirm behavior.

Copilot can propose an implementation for the HTTP call and response parsing based on the surrounding code and patterns in your repository because it leverages local context to suggest idiomatic code that issues the request parses JSON and applies appropriate timeouts. This gives you a solid starting point that you can refine and validate against the real API.

Apigee will auto generate complete client libraries and test suites for any external API so you do not need to write client code is incorrect because Apigee focuses on API management and it does not automatically create full client SDKs and comprehensive tests for arbitrary external services.

Copilot removes the need to read the provider documentation or call the API yourself is incorrect because you must still understand the API contract validate responses and verify the integration with real calls.

Copilot will perfectly infer the provider’s schema and produce exact request and parsing code for every endpoint is incorrect because Copilot suggestions can be imperfect or incomplete and they do not guarantee exact knowledge of third party schemas.

The correct option is Disable GitHub Copilot for the specific repository folders or files that contain regulated data. This ensures those files are not used as context by the service so their contents are not transmitted when suggestions are generated.

This approach addresses the requirement at the point where data would be sent from the workstation. Copilot generates suggestions by using the open file and related context in the editor. When you turn Copilot off for those locations or remove access for the repository, the editor stops sending that content so any PII remains local. Organizations can enforce this by disabling Copilot on affected repositories and developers can disable Copilot per workspace or by language in their IDE when working with sensitive files.

Use Google Cloud Sensitive Data Protection in Cloud DLP to scan commits and block PII before merges is not sufficient because it acts after code is written and does not stop the IDE from sending file contents to Copilot during suggestion generation.

Rely on GitHub Copilot default privacy settings to automatically filter out sensitive values from context is incorrect because Copilot still uses the open file content as context and there is no guarantee that sensitive data is never sent unless the feature is disabled where that data resides.

Insert a comment at the top of the file asking Copilot not to read the following section is ineffective because ad hoc comments do not control what the extension transmits and the service does not rely on such comments to gate data flow.

Host recurring peer showcases where developers share successful prompts and outcomes in the team’s chat and meetings most closely aligns with the goal because it centers on everyday communication and shared language rather than tooling changes or formal training.

This approach builds a common vocabulary for prompts and encourages social learning through conversation. It normalizes usage in the channels developers already use and invites lightweight participation that reinforces good prompting habits over time. It also creates rapid feedback loops where people learn from real examples and adapt them to their own work, which is exactly how Copilot adoption grows in practice without new tools or structured courses.

Publish a detailed handbook with prompt guidelines and team-specific best practices relies on static documentation and does not emphasize live conversation or peer exchange, so it is less effective for driving everyday language and communication around Copilot usage.

Cloud Build does not relate to communication patterns for Copilot adoption and is not a mechanism for encouraging prompt sharing or discussion.

Integrate Copilot into the standard IDE images and project workflows so it is available by default focuses on tooling and rollout mechanics rather than on the conversational practices that build prompt literacy and sustained usage.

The correct answer is Copilot Business audit logs record user and administrator activity which helps teams spot unauthorized access and perform access reviews.

This is correct because audit logs are designed to capture who did what and when across an organization. They provide visibility into user actions and administrative changes which supports access reviews and investigations. This telemetry helps demonstrate compliance with access management policies by showing a traceable record of relevant events.

Copilot Business audit logs reveal the reasoning behind each AI suggestion so reviewers can understand why a completion was generated is incorrect because audit logs do not expose the model’s internal reasoning or explain why a particular completion was produced. They record activity events rather than the content or rationale of AI suggestions.

Copilot Business audit logs automatically block suspicious sign-ins and enforce multi factor authentication without any additional security tooling is incorrect because audit logs are observational and do not enforce policies or block access. Enforcement of sign in policies and multi factor authentication is handled by identity and access controls rather than by the audit log itself.

Copilot Business audit logs provide detailed error diagnostics for code suggestions to simplify organization wide troubleshooting is incorrect because audit logs are not detailed error or debugging logs for code completions. They track activity and changes for accountability and review rather than low level troubleshooting data.

The correct option is Enable Copilot filter that blocks public code matches.

This setting turns on the public code filter in GitHub Copilot so suggestions that closely match public repositories are suppressed while Copilot continues to generate other AI suggestions. The public code filter compares candidate completions with public code and removes near matches which satisfies the requirement to block matches to public code without disabling Copilot.

Enable GitHub Advanced Security is unrelated to Copilot suggestion filtering. It provides code scanning, secret scanning, and dependency features and it does not control whether Copilot blocks matches to public code.

Enable Cloud DLP in Google Cloud is a Google Cloud service and it does not configure GitHub Copilot or any GitHub.com setting for suggestion filtering.

Set organization repository exclusions in Copilot controls where Copilot can be used or how content is shared but it does not provide the specific capability to block suggestions that match public code while keeping AI completions enabled.

The correct option is Keep only the files you are actively working on open and close unrelated files to shrink the context Copilot Chat processes.

Copilot Chat draws context from your open tabs, your selections, and the repository index in your IDE. By limiting the number of open files you reduce the amount of code the assistant must scan which lowers token usage and speeds retrieval. This keeps the conversation focused on the task at hand and typically improves responsiveness in large monorepos.

Paste larger code blocks into each prompt so Copilot Chat sees entire functions and classes is counterproductive because larger pastes increase token usage and slow responses, and they can crowd out relevant context or hit context limits.

Redesign the codebase to remove cross module dependencies so each file stands alone for Copilot Chat is unnecessary and impractical for a monorepo and it does not address how the assistant collects context in the IDE. Focused context management is the effective lever rather than large architectural changes.

Move the repository to Cloud Source Repositories to reduce latency for Copilot Chat will not help because Copilot Chat performance depends on prompt size, context retrieval, and IDE integration rather than the hosting provider. Cloud Source Repositories has been retired which makes this option even less likely to appear as correct on newer exams.

The correct option is Organization audit logs show which users enabled or accessed Copilot and record administrative changes such as seat or subscription updates.

This is correct because the organization audit log records Copilot related events that are important for governance. Administrators can see when a member was granted Copilot access or when a user enabled Copilot and they can review changes to licensing such as seat assignments and subscription configuration. Each event includes who performed the action, what changed, and when it occurred which supports security investigations and compliance reviews. These entries can be searched, exported, and streamed which makes ongoing oversight practical.

Organization audit logs push immediate alerts about code quality issues in Copilot suggestions is incorrect because the audit log does not evaluate or alert on code quality. It records administrative and security relevant events rather than analyzing the content of suggestions.

Organization audit logs capture the full text of Copilot outputs for later human review is incorrect because GitHub does not store or expose the generated code or suggestions in the audit log. The log contains metadata about actions and access, not the content of completions.

Organization audit logs track the count of Copilot prompts per user so administrators can enforce per user rate limits is incorrect because the audit log does not provide per user prompt counting for rate limiting. Usage analytics may exist in other reports, but the audit log is focused on recording discrete administrative and access events.

The correct option is Write a structured prompt that enumerates weight distance and service level with sample inputs unit assumptions and the exact output shape.

This approach tells Copilot exactly which inputs to consider and what units they use, which formulas or tiers to apply, and how the result must be formatted. When you include representative examples and edge cases along with the precise shape of the return object, Copilot can reason through scenarios like zero or negative inputs, maximum thresholds, surcharges, and rounding. It can also produce a stable object that contains per component breakdowns and totals so the output is consistent and easy to test.

Specifying the output schema up front helps Copilot align variable names, types, and field descriptions. Declaring assumptions such as currency, distance units, and whether taxes are included reduces ambiguity and leads to code that is safer and easier to maintain.

Paste a partially written function and rely on Copilot to fill the gaps based on common e commerce patterns is weaker because Copilot will mirror the incomplete structure and may invent defaults or miss critical rules. It often yields code that seems plausible but does not enforce units, validation, or a consistent return shape.

Use Vertex AI Codey to generate the function instead of improving the Copilot prompt does not solve the real problem which is prompt clarity. Switching tools can still produce incomplete or inconsistent logic if the requirements, assumptions, and output contract are not explicit.

Keep the request broad and trust Copilot to infer sophisticated pricing logic from prior training leads to generic implementations that skip edge cases and do not return a clear breakdown object. Broad requests usually produce simplistic code rather than a thorough and reliable calculation.

The correct answers are Write a natural language comment in a JavaScript file then pause and press Tab to accept the inline suggestion that appears and Run gh copilot suggest in the terminal to request a code suggestion from the GitHub CLI.

Writing a natural language comment gives Copilot rich intent and context and it will often propose an inline completion in Visual Studio Code. You can then accept the suggestion with Tab which is a standard workflow for inline completions.

Using the GitHub CLI, the gh copilot suggest command lets you request a suggestion directly from the terminal. This action queries Copilot and returns a proposed snippet that you can review and use.

Press Ctrl+Space on a blank line in a Python module to trigger a suggestion is not correct because Ctrl+Space triggers the editor IntelliSense and not Copilot, and on a blank line there is often no meaningful suggestion. Copilot suggestions are invoked as you type or by using Copilot specific triggers or by providing context such as a descriptive comment.

Let a runtime error occur in a Go program and wait for Copilot to automatically insert a fix is not correct because Copilot does not modify your code automatically in response to runtime errors. You must request help in the editor or through chat and then choose whether to apply a suggested change.

The correct option is Copilot Chat grounded by company knowledge.

Enterprise adds an organization aware knowledge capability that lets chat use your private repositories and approved knowledge sources so it can tailor answers to your code and content. Business offers chat across GitHub and supported IDEs yet it does not include this organization wide knowledge grounding feature.

Private fine tuning on private repositories is incorrect because Copilot does not provide customer specific model fine tuning and neither plan offers it. Copilot relies on contextual grounding and policy controls rather than training a separate model on your code.

On premises deployment is incorrect because Copilot is a cloud hosted service and there is no fully on premises deployment option.

Full offline mode is incorrect because Copilot requires internet connectivity to reach the service and a complete offline experience is not supported.