Free GH-300 GitHub Copilot Practice Tests

Your team at a digital publishing startup called Riverstone Media is drafting responsible AI practices for a code assistant built on Vertex AI. You want to match a realistic generative AI risk with a mitigation that meaningfully reduces harm during development and review. Which pairing is the most appropriate?

• ❏ A. Harm is that Copilot style suggestions are sometimes incomplete and mitigation is to rely entirely on user feedback to fix issues

• ❏ B. Harm is that generated output can exhibit gender and racial bias and mitigation is to run fairness evaluations and apply post processing filters before release

• ❏ C. Harm is that code produced by the model fails to compile and mitigation is to increase the model temperature in Vertex AI

• ❏ D. Harm is that generated code may contain copyrighted snippets and mitigation is to enable Cloud DLP scanning on responses

Your team at mcnz.com is setting up new developer workstations and needs to enable GitHub Copilot commands within the GitHub CLI. Which command installs the Copilot CLI extension so that gh copilot subcommands become available?

• ❏ A. npm install -g github-copilot

• ❏ B. gcloud components install copilot

• ❏ C. gh extension install github/gh-copilot

• ❏ D. gh copilot install

You maintain a Fastify-based authentication service for scrumtuous.com with /signin and /signup endpoints. Current integration tests only cover successful responses and do not validate malformed input, missing JSON fields, or temporary lockouts after repeated failures. You plan to use GitHub Copilot to expand the suite and improve coverage of these edge conditions. What is an effective way to use GitHub Copilot to generate and refine the additional tests?

• ❏ A. Rely on Copilot to generate only happy-path integration tests because those flows provide the most business value

• ❏ B. Enable Cloud Armor rate limiting on the service endpoint and skip writing edge-case tests because platform controls will cover those scenarios

• ❏ C. Write descriptive comments that list negative scenarios like invalid emails, absent passwords, malformed JSON bodies, and lockouts after four failed attempts so Copilot proposes corresponding tests to review and refine

• ❏ D. Ask Copilot to refactor the service code so it handles edge cases automatically and assume that will make the tests comprehensive

A development group at scrumtuous.com is assessing GitHub Copilot for daily coding assistance and they want clarity on its limits around reasoning ability, the freshness of its knowledge, and how it interprets prompts. Which statement most accurately reflects these limitations?

• ❏ A. When integrated with Vertex AI Search Copilot gains real time knowledge and performs symbolic reasoning for exact answers

• ❏ B. GitHub Copilot continuously learns from live repositories and therefore delivers current code with rigorous logical reasoning

• ❏ C. GitHub Copilot is trained on static data so it does not truly reason it cannot be trusted for calculations and it may surface older patterns and misread ambiguous prompts

• ❏ D. Copilot relies only on curated snippets and applies advanced symbolic reasoning that guarantees secure and compliant code although formatting might be inconsistent

When working across repositories and pull requests on GitHub.com, what is the primary advantage of using Copilot Chat?

• ❏ A. Chat-triggered merges for pull requests

• ❏ B. Automatic synchronization of GitHub Actions secrets into Copilot

• ❏ C. In-context understanding of PRs and repo content on GitHub.com

• ❏ D. One-click Cloud Run deployments from chat

The security team at Brightwood Labs manages a GitHub Enterprise Cloud organization. Which organization level control can they use to decide where GitHub Copilot generates code suggestions across teams and repositories?

• ❏ A. Limit Copilot usage to specific daily hours

• ❏ B. Cloud Identity

• ❏ C. Enable or disable code suggestions scoped to specific teams or repositories

• ❏ D. Use commit history filters to restrict suggestions per file

A developer at scrumtuous.com needs GitHub Copilot to respond like a cloud security analyst while reviewing a Terraform configuration so that suggestions reflect that expertise. What prompt technique best describes this approach?

• ❏ A. Instructing GitHub Copilot to use a friendly or formal tone in its replies

• ❏ B. Asking GitHub Copilot to explain the purpose of a suggested change in the code

• ❏ C. Directing GitHub Copilot to adopt a specific professional persona within the prompt to shape its output

• ❏ D. Configuring IAM roles in Google Cloud to limit user permissions before prompting

A development group at Aurora Systems plans to deploy GitHub Copilot Business across their projects and they want a contractual assurance that protects them from third-party intellectual property claims related to code produced by Copilot. Which provision in the GitHub Copilot Business terms provides this protection?

• ❏ A. GitHub Copilot Business automatically filters out any suggestion that appears similar to open source code to avoid conflicts

• ❏ B. GitHub Copilot Business includes indemnification that protects against third-party IP claims for Copilot generated code

• ❏ C. Enabling the duplication detection filter for Copilot suggestions

• ❏ D. Assured Open Source Software

At a fintech startup you are working in Visual Studio Code on a TypeScript service and one source file has grown to about 2,800 lines. As you edit near the bottom of that file you notice GitHub Copilot suggestions feel unrelated to patterns defined earlier in the file. What is the most likely reason for this behavior?

• ❏ A. Copilot supports only UI frameworks and ignores backend code

• ❏ B. You must retrain or fine tune Copilot on your repository to improve suggestions

• ❏ C. The file length exceeds the amount of code that Copilot can consider at once

• ❏ D. Copilot is incompatible with TypeScript projects

During a Node.js to TypeScript refactor, the team finds that Copilot is reproducing legacy antipatterns. How should they use it most effectively?

• ❏ A. Disable Copilot for the migration

• ❏ B. Use Copilot for scaffolding then manually refactor complex and risky code

• ❏ C. Use Copilot to fully rewrite the codebase without review

• ❏ D. Use GitHub Copilot Chat to auto apply refactors across the entire repo

You are the engineering lead at Riverbend Labs and your group has 24 developers who contribute to public open source repositories and maintain proprietary applications in private repositories. You plan to roll out GitHub Copilot to boost delivery while keeping licensing costs reasonable and you need a plan that supports team management across both repository types. Which GitHub Copilot plan best fits these requirements?

• ❏ A. GitHub Copilot for Business

• ❏ B. GitHub Copilot for Education

• ❏ C. GitHub Copilot for Teams

• ❏ D. GitHub Copilot Free for individuals

Your development group at Apex Motors is evaluating GitHub Copilot Chat to get inline code help and troubleshoot issues through the chat panel. You want to know what information the client sends and how GitHub services handle it during a typical conversation. Which statement best describes the data flow for GitHub Copilot Chat?

• ❏ A. Prompts and code snippets are sent to GitHub and retained to train the underlying models

• ❏ B. The chat client sends prompts and relevant context to GitHub services for ephemeral processing and data from private repositories is not stored or used for model training

• ❏ C. GitHub Copilot Chat processes all interactions locally on the developer machine and never transmits any data to GitHub

• ❏ D. The chat client invokes Vertex AI in your Google Cloud project and persists prompts in Cloud Logging

Solstice Retail Group limits GitHub Copilot for Business to specific engineering squads, yet you believe some unapproved users have enabled it and you plan to analyze the organization audit log to verify this. Which activity can be surfaced in GitHub audit logs to help you identify unauthorized Copilot usage?

• ❏ A. A developer accepts a Copilot suggestion in the editor

• ❏ B. An administrator grants or removes a Copilot Business seat

• ❏ C. A user commits code generated by Copilot to a repository

• ❏ D. A user installs the Copilot Chat extension in their IDE

At scrumtuous.com your team maintains a JavaScript helper called maxValue that scans an array to return the largest number and it returns undefined when the array is empty. You want GitHub Copilot to suggest a thorough set of unit tests that exercise corner cases including empty input a single element repeated values mixed negative and positive values and very large numbers. What is the best way to prompt Copilot to get comprehensive edge case suggestions?

• ❏ A. Cloud Build

• ❏ B. Tell Copilot to generate tests only for arrays with positive values and ignore zeros and negatives

• ❏ C. Ask Copilot to enumerate edge focused tests including empty arrays single item arrays duplicates mixtures of negative and positive values and extreme numeric values

• ❏ D. Have Copilot write one happy path test with two numbers and plan to add more tests manually later

Copilot is producing off topic and inconsistent code suggestions. Which prompt engineering principle is being overlooked?

• ❏ A. Iteratively refine the prompt

• ❏ B. Use few shot examples

• ❏ C. Provide clear instructions and sufficient context

• ❏ D. Set repository style settings

A fintech startup named LumaPay is evaluating GitHub Copilot Business for its engineering team so it can reduce intellectual property risk and streamline payment management for many developers. Which statement describes a benefit that is unique to GitHub Copilot Business when compared to the Individual plan?

• ❏ A. Copilot Business provides end to end encryption while Copilot Individual does not encrypt data in transit or at rest

• ❏ B. Copilot Business lets organizations consolidate user licenses into one centrally managed invoice

• ❏ C. Copilot Business offers a bring your own data capability to train or fine tune the model on proprietary repositories

• ❏ D. Both Individual and Business plans allow unlimited usage across multiple GitHub accounts for a single user to support team flexibility

At NovaLedger, a finance startup building a confidential trading tool, you plan to enable GitHub Copilot in your IDE for a private repository. How does GitHub Copilot handle the code context and prompts that you provide while it generates suggestions?

• ❏ A. GitHub Copilot forwards your prompts to Google Vertex AI endpoints in your Google Cloud project and data handling follows your project retention policies

• ❏ B. GitHub Copilot uploads all of your repository code including private files to the cloud for processing and long term storage

• ❏ C. GitHub Copilot sends the minimum necessary snippets of your current context to GitHub servers to produce suggestions and it does not retain this content for future model training

• ❏ D. GitHub Copilot performs all analysis locally on your machine and it never sends code or metadata to any external service

You are a backend developer at a digital payments startup building a checkout service that handles transaction processing, input validation, and integrations with a third party payments API. Your team uses GitHub Copilot to accelerate delivery. You must implement a security sensitive module that validates cardholder inputs and enforces transaction integrity when calling the provider at example.com. What is a realistic limitation of using GitHub Copilot for this work?

• ❏ A. Security Command Center can automatically block any insecure patterns that Copilot might generate so manual security review is not needed

• ❏ B. Copilot can recommend secure approaches yet it may still output code with subtle vulnerabilities that require careful human review

• ❏ C. Copilot always produces code that is perfectly tuned for high security scenarios and peak performance

• ❏ D. Copilot will detect and repair every security weakness in the produced code which makes human code review unnecessary

While using GitHub Copilot Chat in your IDE, what practice most effectively helps it produce faster and more focused code suggestions?

• ❏ A. Turn off syntax highlighting

• ❏ B. Edit within a concise file that contains only the logic you are asking about

• ❏ C. Remove every comment from the workspace

• ❏ D. Temporarily disable Git integration in the editor

Which statement accurately describes a core privacy practice for GitHub Copilot?

• ❏ A. Copilot Business must enable IP indemnification to meet privacy requirements

• ❏ B. Copilot keeps all user prompts for 90 days to retrain models

• ❏ C. Users and organizations can opt out of Copilot telemetry and interaction data sharing

• ❏ D. Copilot sends user code to third-party clouds outside Microsoft

A development team at scrumtuous.com plans to roll out GitHub Copilot and wants clarity on how its duplication filter reduces the chance of verbatim public code appearing in suggestions. Which statement best describes how this filter behaves?

• ❏ A. The duplication detector blocks any code snippet that exceeds eight lines to avoid copyright issues

• ❏ B. The duplication filter compares proposed completions against public repositories and suppresses suggestions that are exact matches

• ❏ C. The duplication filter is enforced only for GitHub Copilot Business and Enterprise so organizations can maintain intellectual property compliance

• ❏ D. The duplication filter guarantees that Copilot suggestions never resemble any public code even partially

At mcnz.com your developers currently use GitHub Copilot for Individuals and they are moving a mission critical app to a private repository that contains proprietary code. They need stronger privacy protections with central policy enforcement and fine grained permission management. Which GitHub Copilot subscription should they choose?

• ❏ A. Choose GitHub Copilot Free Plan to minimize costs while continuing to use Copilot in both public and private repositories

• ❏ B. Adopt GitHub Copilot for Business to gain centralized management advanced privacy options and full support for private repositories

• ❏ C. Select GitHub Copilot Enterprise to integrate with GitHub Enterprise Cloud and enable enterprise governance and knowledge features

• ❏ D. Keep GitHub Copilot for Individuals because it already works with private repositories and offers enough security for sensitive code

At Calypso Logistics, a distributed platform with many microservices has fallen behind on clear inline comments, docstrings, and project READMEs, and the engineers want to use GitHub Copilot to raise documentation quality yet they are unsure how to apply it effectively. Which practice would most help them get accurate and maintainable documentation from Copilot?

• ❏ A. Set up Cloud Build to auto-generate documentation from source and treat that output as complete without human review

• ❏ B. Use GitHub Copilot to draft comments and READMEs then have developers revise and tailor the wording for the codebase and domain

• ❏ C. Turn off Copilot for docs and require all documentation to be written by hand to ensure quality

• ❏ D. Allow Copilot to produce full documentation for each file and skip any manual edits to save time

You are writing a Python helper that computes the invoice total for an online cart, including VAT and a promotional coupon for example.com. You provide a short instruction to GitHub Copilot that says “Function to compute invoice total with VAT and coupon”, and the suggestion calculates tax but omits the coupon. What adjustment to your prompt would most likely guide Copilot to include the discount logic?

• ❏ A. State the language explicitly in the prompt such as “Write a Python function that computes an invoice total including VAT and a coupon”

• ❏ B. Include precise values and constraints in the prompt such as “use 12% VAT and apply a 7% coupon”

• ❏ C. Paste several input and output examples of test cases into the prompt

• ❏ D. Shorten the request to “Function to compute cart total”

Which gh copilot command offers interactive suggestions in the terminal?

• ❏ A. gh copilot deploy

• ❏ B. gh copilot suggest

• ❏ C. gh repo clone

• ❏ D. gh copilot refactor

The correct pairing is Harm is that generated output can exhibit gender and racial bias and mitigation is to run fairness evaluations and apply post processing filters before release.

Bias in generative models is a well known risk for code assistants and it can surface in comments, identifier names, or generated explanations that reflect stereotypes. Running fairness evaluations during development helps identify disparate outcomes and harmful patterns across groups. Applying post processing filters and safety settings in Vertex AI reduces exposure to biased content before users encounter it. Using evaluation plus filtering provides proactive and measurable safeguards during development and review, which aligns with the goal of reducing harm before release.

Harm is that Copilot style suggestions are sometimes incomplete and mitigation is to rely entirely on user feedback to fix issues is not appropriate because incompleteness is a routine product quality issue rather than a responsible AI harm. Relying entirely on user feedback is reactive and does not provide a systematic mitigation during development or review.

Harm is that code produced by the model fails to compile and mitigation is to increase the model temperature in Vertex AI is incorrect because compilation failures are a functional quality problem rather than an ethical or safety risk. Increasing temperature raises randomness and would likely worsen consistency rather than improve compilability.

Harm is that generated code may contain copyrighted snippets and mitigation is to enable Cloud DLP scanning on responses is not suitable because Cloud DLP detects sensitive data such as PII or secrets and it does not detect copyright status. Addressing copyright concerns requires provenance controls, licensing policies, or specialized content provenance checks rather than DLP.

The correct option is gh extension install github/gh-copilot.

This command uses the GitHub CLI extension system to fetch and install the official Copilot extension from the github organization repository. Installing the extension adds the Copilot subcommands to the GitHub CLI so you can run Copilot commands through gh.

npm install -g github-copilot is incorrect because Copilot in the GitHub CLI is delivered as a gh extension and not as a global Node package.

gcloud components install copilot is incorrect because that is the Google Cloud SDK and it does not manage GitHub CLI extensions.

gh copilot install is incorrect because there is no built-in copilot subcommand before the extension is installed and installation is done through the gh extension install mechanism.

The correct option is Write descriptive comments that list negative scenarios like invalid emails, absent passwords, malformed JSON bodies, and lockouts after four failed attempts so Copilot proposes corresponding tests to review and refine.

This approach directs Copilot with clear intent so it can propose tests that target the missing edge conditions. By enumerating invalid emails, missing fields, malformed bodies, and temporary lockout behavior in comments near the test suite, Copilot can suggest relevant test cases that you can review, adjust, and finalize. This leads to broader and more reliable coverage of your Fastify authentication endpoints because the generated tests are anchored to explicit scenarios and expected outcomes.

You can iteratively refine the suggestions by accepting one or two proposed tests, running them, and then adding or adjusting comments to capture any gaps. This review loop ensures the tests reflect your real requirements and prevents overreliance on auto generation while still accelerating the work.

Rely on Copilot to generate only happy-path integration tests because those flows provide the most business value is incorrect because it does not address malformed input, missing fields, or lockout behavior and it would leave critical risk untested.

Enable Cloud Armor rate limiting on the service endpoint and skip writing edge-case tests because platform controls will cover those scenarios is incorrect because rate limiting does not validate input formats or required fields and platform controls cannot substitute for application-level tests that verify authentication behavior.

Ask Copilot to refactor the service code so it handles edge cases automatically and assume that will make the tests comprehensive is incorrect because changing code does not create tests and you still need explicit, scenario-driven tests to verify behavior and prevent regressions.

The correct option is GitHub Copilot is trained on static data so it does not truly reason it cannot be trusted for calculations and it may surface older patterns and misread ambiguous prompts.

This statement is accurate because Copilot is powered by large language models that learn from a fixed training set and do not update their knowledge in real time. These models generate predictions based on patterns rather than performing step by step symbolic reasoning, so they can be unreliable for precise calculations or strict logical proofs. Since the training data reflects code available at the time of training, suggestions can mirror older practices. When prompts are ambiguous the model may infer an unintended direction, so clear and specific instructions help reduce misinterpretation.

When integrated with Vertex AI Search Copilot gains real time knowledge and performs symbolic reasoning for exact answers is incorrect because adding a separate search service does not convert Copilot into a system with real time knowledge or symbolic reasoning. Copilot remains a probabilistic generator and it does not provide exact guarantees.

GitHub Copilot continuously learns from live repositories and therefore delivers current code with rigorous logical reasoning is incorrect because Copilot does not continuously learn from your repositories and it only uses your open files and context during a session. Its training is not updated from your code in real time and its outputs are not the result of rigorous logical reasoning.

Copilot relies only on curated snippets and applies advanced symbolic reasoning that guarantees secure and compliant code although formatting might be inconsistent is incorrect because Copilot is trained on broad public data rather than only curated snippets and it does not perform symbolic reasoning. It cannot guarantee security or compliance, so all outputs require review.

The correct option is In-context understanding of PRs and repo content on GitHub.com.

On GitHub.com, Copilot Chat uses in-context understanding of the open pull request and repository content to explain code, summarize diffs, and assist with reviews. This grounded context improves collaboration across repositories and pull requests because the assistant can reference files, discussions, and history without manual copy and paste while respecting repository permissions.

Chat-triggered merges for pull requests is not a capability of Copilot Chat on GitHub.com. Merging still follows GitHub workflow controls and required permissions and is not executed directly from chat.

Automatic synchronization of GitHub Actions secrets into Copilot is not a feature and would be unsafe. Copilot Chat does not access or sync encrypted secrets and it relies on the repository and pull request context you open.

One-click Cloud Run deployments from chat is not supported by Copilot Chat. Cloud Run is a Google Cloud service and deployments are performed through configured CI workflows or cloud tooling rather than chat.

The correct option is Enable or disable code suggestions scoped to specific teams or repositories.

GitHub Enterprise Cloud provides organization level Copilot policies that let admins decide where suggestions are allowed. You can target specific repositories and grant access by teams or selected members, which effectively controls where Copilot generates code suggestions across the organization.

Limit Copilot usage to specific daily hours is not available in GitHub Copilot administration. There is no scheduling feature at the organization level to enable or disable Copilot by time of day.

Cloud Identity is a Google identity service and it does not provide organization controls for GitHub Copilot. You can integrate external identity providers for single sign on, yet that does not decide where Copilot can generate suggestions.

Use commit history filters to restrict suggestions per file is not a Copilot policy. GitHub offers policies like blocking suggestions that match public code and scoping by repository or team rather than commit history or per file filtering.

The correct option is Directing GitHub Copilot to adopt a specific professional persona within the prompt to shape its output.

Assigning a role like cloud security analyst guides the model to prioritize risk identification, secure defaults, and compliance minded reasoning when reviewing Terraform. This approach narrows the context and criteria that Copilot uses so the suggestions and explanations align with the requested expertise.

This technique is a core prompt engineering pattern because role or persona instructions influence both the depth and the focus of the analysis. It helps the model surface security relevant checks and provide justifications that match how a specialist would review infrastructure as code.

Instructing GitHub Copilot to use a friendly or formal tone in its replies is incorrect because tone only changes style and voice. It does not provide the domain specific perspective needed for security focused Terraform reviews.

Asking GitHub Copilot to explain the purpose of a suggested change in the code is incorrect because explanation improves clarity but it does not direct the model to use cloud security analyst criteria when forming suggestions.

Configuring IAM roles in Google Cloud to limit user permissions before prompting is incorrect because access controls in the cloud environment do not shape how Copilot generates code review suggestions within the editor.

The correct option is GitHub Copilot Business includes indemnification that protects against third-party IP claims for Copilot generated code.

This is the only choice that offers a contractual promise. The GitHub Copilot Business product specific terms include an indemnity where GitHub agrees to defend and cover certain losses from third party intellectual property claims that are asserted against code output produced by the service. This matches the requirement for a contract backed assurance rather than a technical control.

GitHub Copilot Business automatically filters out any suggestion that appears similar to open source code to avoid conflicts is incorrect because Copilot does not guarantee automatic removal of all content that resembles open source code. Filtering features are designed to reduce risk but they are not comprehensive and they are not a contract.

Enabling the duplication detection filter for Copilot suggestions is incorrect because it is a technical safeguard that can reduce near duplicate suggestions but it does not provide legal protection. It is not a substitute for indemnification.

Assured Open Source Software is incorrect because it is a separate Google offering focused on vetted open source dependencies and is unrelated to GitHub Copilot and it does not provide indemnity for Copilot generated code.

The correct option is The file length exceeds the amount of code that Copilot can consider at once. When you edit near the bottom of a very long file the model can only attend to a limited window of surrounding code, so earlier patterns fall outside that window and suggestions feel less connected to the rest of the file.

Copilot bases its completions on the code and comments around your cursor and on other nearby or open files. All large language models work within a fixed context window, which means only a portion of the file can be included in the prompt at any time. In a 2,800 line TypeScript file the sections near the top are likely outside the active context while you edit near the bottom, so the assistant cannot reliably use those earlier patterns when proposing completions.

Copilot supports only UI frameworks and ignores backend code is incorrect because Copilot works across many languages and domains, including backend TypeScript and server side frameworks, and it does not limit support to UI code.

You must retrain or fine tune Copilot on your repository to improve suggestions is incorrect because users do not retrain or fine tune Copilot on their own data. Copilot improves suggestions by using the immediate coding context in your editor rather than by per repository model training.

Copilot is incompatible with TypeScript projects is incorrect because Copilot fully supports TypeScript and is widely used in TypeScript codebases. The issue in this scenario stems from context limits, not language compatibility.

The correct answer is Use Copilot for scaffolding then manually refactor complex and risky code.

This approach leverages Copilot where it is strongest which is quickly producing boilerplate and repetitive patterns that are easy to verify while keeping humans in control of intricate logic. During a Node.js to TypeScript migration the team can let Copilot draft types, interfaces, and simple conversions and then rely on strict compiler checks, targeted tests, and careful code review for the high risk paths. This balances speed with safety and it reduces the chance that legacy antipatterns slip into the new TypeScript code.

Copilot Chat can still assist by explaining type errors, suggesting incremental steps, and generating small refactoring snippets. The team should run the compiler in strict mode, add tests around critical areas, and review changes in pull requests so that automated suggestions are validated before merging.

Disable Copilot for the migration is incorrect because turning the tool off forfeits useful assistance for boilerplate and simple transformations. Using it selectively provides value while maintaining control over complex work.

Use Copilot to fully rewrite the codebase without review is incorrect because unreviewed wholesale rewrites are risky and can propagate antipatterns or introduce regressions. Human review and tests are needed to ensure correctness and maintainability.

Use GitHub Copilot Chat to auto apply refactors across the entire repo is incorrect because Chat does not safely apply bulk refactors across a repository on its own. It is a conversational assistant that proposes changes which still require developer oversight and verification.

The correct option is GitHub Copilot for Teams because it provides organization level seat and policy management across both public and private repositories while keeping per seat pricing appropriate for a group of 24 developers.

This plan lets you centrally assign and revoke licenses, manage access as teams change, and apply policies that cover usage in private code as well as contributions to open source. It supports collaborative administration without requiring the higher cost and controls that are aimed at very large enterprises.

GitHub Copilot for Business includes advanced enterprise controls and security and it is priced for larger organizations, which makes it more than you need when the goal is reasonable licensing costs and straightforward team management for 24 developers.

GitHub Copilot for Education is intended for verified academic programs and students and faculty, so it does not fit a commercial team at Riverbend Labs.

GitHub Copilot Free for individuals is a single user plan and it lacks organization billing, team management, and policy controls, so it cannot be used to centrally roll out and manage access across private repositories for your team.

The correct option is The chat client sends prompts and relevant context to GitHub services for ephemeral processing and data from private repositories is not stored or used for model training.

This choice reflects how Copilot Chat operates. The client sends your prompt together with relevant editor context to GitHub services where it is processed to generate a response. The processing is transient for service delivery and safety checks. For Copilot Business and Enterprise, content from private repositories and prompts are not used to train the underlying models and GitHub provides controls that keep customer code out of model training. Providers may retain minimal data for abuse detection and diagnostics for a limited time which does not change the fact that your private code is not used to train models.

Prompts and code snippets are sent to GitHub and retained to train the underlying models is incorrect because GitHub states that private repository content and prompts from Copilot for Business and Enterprise are not used to train foundation models. Any optional data sharing to improve the product is off by default for these plans and does not change the default training exclusion for private code.

GitHub Copilot Chat processes all interactions locally on the developer machine and never transmits any data to GitHub is incorrect because Copilot Chat relies on GitHub services and hosted language models. The client must transmit prompts and context to the service in order to obtain a completion.

The chat client invokes Vertex AI in your Google Cloud project and persists prompts in Cloud Logging is incorrect because GitHub Copilot Chat uses GitHub services and approved model providers and it does not route through your Google Cloud project or persist prompts in Cloud Logging.

The correct option is An administrator grants or removes a Copilot Business seat.

GitHub organization audit logs record administrative changes that affect access to services. Copilot Business seat assignments and removals are captured as audit events with details about who performed the action, which user was affected, and when it occurred. These entries allow you to identify unauthorized access by reviewing who was granted a seat and to whom it was removed.

A developer accepts a Copilot suggestion in the editor is an IDE interaction and is not emitted to the GitHub organization audit log. GitHub does not log per suggestion acceptance events at the organization level.

A user commits code generated by Copilot to a repository cannot be uniquely identified in the audit log because commits are not labeled as Copilot generated. They appear like any other user authored commit and therefore do not help confirm unauthorized Copilot usage.

A user installs the Copilot Chat extension in their IDE occurs within the local development environment or the IDE marketplace and is not tracked in the GitHub organization audit log.

The correct option is Ask Copilot to enumerate edge focused tests including empty arrays single item arrays duplicates mixtures of negative and positive values and extreme numeric values. This prompt clearly directs Copilot to cover the full set of boundary conditions for the maxValue helper and it aligns with the requirement to return undefined for an empty array.

This choice is effective because it specifies the classes of inputs that commonly reveal defects. By asking for empty arrays single item arrays duplicates mixed signs and extremes you guide Copilot to propose tests that exercise both typical and pathological cases. Being explicit about these categories helps Copilot generate a comprehensive suite rather than a few generic examples and it increases the likelihood that the tests will validate the expected undefined result on empty input.

Cloud Build is unrelated to GitHub Copilot prompting and it is a Google Cloud continuous integration and delivery service rather than a method for eliciting unit test suggestions. It does not help you author edge case tests for a JavaScript helper.

Tell Copilot to generate tests only for arrays with positive values and ignore zeros and negatives is wrong because it restricts the input space and omits critical edge cases. Ignoring zeros and negatives would miss the mixed value scenarios that the question explicitly requires.

Have Copilot write one happy path test with two numbers and plan to add more tests manually later fails to request a thorough set of edge cases. A single happy path test does not explore empty input single element duplicates mixed signs or very large numbers and it does not meet the stated goal.

The correct option is Provide clear instructions and sufficient context.

This principle directly addresses off topic and inconsistent code suggestions because the model relies on explicit goals, relevant code snippets, filenames, frameworks, versions, and constraints to stay grounded. When you specify what to do, where to do it, and any relevant boundaries, the assistant can remain aligned with your intent and produce consistent results.

Iteratively refine the prompt helps improve results over multiple attempts, yet it does not fix the core problem described here. If the initial request lacks clarity and context then repeated refinement may still wander because the model never had the necessary grounding to begin with.

Use few shot examples can be helpful for shaping style or demonstrating a pattern, but it is not the root solution when outputs are off topic. Without clear objectives and sufficient contextual details, examples alone cannot prevent drift.

Set repository style settings influences formatting and linting preferences, but it does not determine topical relevance or consistency of generated code. Style configuration cannot substitute for the needed instructions and context.

The correct answer is Copilot Business lets organizations consolidate user licenses into one centrally managed invoice.

This benefit is specific to the Business plan because organizations can centrally purchase and manage seats and receive a single invoice for all users. The Individual plan is billed to a personal account and does not support consolidated invoicing across multiple developers.

Copilot Business provides end to end encryption while Copilot Individual does not encrypt data in transit or at rest is wrong because encryption in transit and at rest is standard for GitHub services and applies to Copilot across plans. Business does not uniquely add basic encryption capabilities.

Copilot Business offers a bring your own data capability to train or fine tune the model on proprietary repositories is wrong because GitHub does not use your private code to train Copilot for Business and the plan does not provide a fine tuning feature with your proprietary data.

Both Individual and Business plans allow unlimited usage across multiple GitHub accounts for a single user to support team flexibility is wrong because Copilot Individual is tied to a single personal account and Business seats are assigned to members within an organization. Licenses are not shared for unlimited use across multiple accounts by one person.

The correct option is GitHub Copilot sends the minimum necessary snippets of your current context to GitHub servers to produce suggestions and it does not retain this content for future model training.

This is accurate because Copilot generates completions by sending only the relevant parts of your current editing context to GitHub operated services that interface with the model. For business and enterprise use GitHub states that prompts and code snippets are not retained or used to train the underlying models. Processing is transient to create the suggestion and while operational telemetry may be collected to run the service your private code is not used for future model training.

GitHub Copilot forwards your prompts to Google Vertex AI endpoints in your Google Cloud project and data handling follows your project retention policies is incorrect because Copilot uses GitHub operated services and the Azure OpenAI Service rather than Google Vertex AI and it does not run inside your own Google Cloud project.

GitHub Copilot uploads all of your repository code including private files to the cloud for processing and long term storage is incorrect because Copilot only transmits the minimal snippets needed from your current context and it does not upload entire repositories nor store your private code for long term retention or model training.

GitHub Copilot performs all analysis locally on your machine and it never sends code or metadata to any external service is incorrect because Copilot relies on cloud hosted inference to generate suggestions which means it must send limited context from your editor to the service.

The correct answer is Copilot can recommend secure approaches yet it may still output code with subtle vulnerabilities that require careful human review.

GitHub Copilot can accelerate implementation and often suggests patterns that align with secure coding guidance. Yet its suggestions are generated from patterns and context rather than a full understanding of your threat model and business rules. This means it can produce code that compiles and appears reasonable while still carrying injection risks, logic errors, or insufficient validation. For a module that validates cardholder inputs and preserves transaction integrity you must perform careful human review, testing, and security analysis before relying on any suggestion.

Security Command Center can automatically block any insecure patterns that Copilot might generate so manual security review is not needed is incorrect because this product focuses on assessing and managing risks in cloud environments and it does not operate in your editor to filter or block generated code. It cannot eliminate the need for manual review of application code.

Copilot always produces code that is perfectly tuned for high security scenarios and peak performance is incorrect because the tool can generate helpful starting points but it does not guarantee optimal security or performance and its output often requires tuning and verification.

Copilot will detect and repair every security weakness in the produced code which makes human code review unnecessary is incorrect because it is not a comprehensive vulnerability scanner or an automatic remediation system. You still need peer review, tests, and security tooling to find and fix issues.

The correct option is Edit within a concise file that contains only the logic you are asking about.

Copilot Chat draws most of its context from the code you have open and especially from what you have selected. Working in a small and focused file reduces unrelated tokens and noise, which helps the model understand your intent quickly and return more relevant suggestions. This practice also improves response speed because there is less extraneous context to process.

Turn off syntax highlighting is incorrect because visual styling in the editor does not influence the model. Copilot reads the underlying text and context rather than colors and themes.

Remove every comment from the workspace is incorrect because comments often communicate intent and constraints that help Copilot generate better suggestions. Deleting all comments can remove useful guidance and is unnecessary.

Temporarily disable Git integration in the editor is incorrect because version control features do not affect how Copilot Chat interprets or generates code. Disabling Git has no meaningful impact on suggestion focus or speed.

The correct option is Users and organizations can opt out of Copilot telemetry and interaction data sharing.

GitHub Copilot provides controls at both the user and organization levels that allow disabling telemetry and limiting the sharing of interaction data. Organization administrators can enforce policies across all seats to restrict data collection, and individual users can adjust their own settings where permitted. These controls reflect a core privacy practice that gives customers meaningful choice over how their data is used.

Copilot Business must enable IP indemnification to meet privacy requirements is incorrect because indemnification addresses intellectual property risk rather than privacy. It is not a privacy control and is not required to satisfy privacy practices for Copilot.

Copilot keeps all user prompts for 90 days to retrain models is incorrect because Copilot for organizations provides settings that prevent using customer content for training, and data retention is limited and purpose bound to service operation and safety rather than blanket model retraining.

Copilot sends user code to third-party clouds outside Microsoft is incorrect because Copilot processes prompts and completions using services operated by GitHub and models hosted on Microsoft Azure, rather than sending code to unrelated third-party cloud providers.

The correct option is The duplication filter compares proposed completions against public repositories and suppresses suggestions that are exact matches.

This is how the matching public code filter works. It checks a proposed suggestion against public code and if the text is an exact duplicate then the suggestion is withheld. This reduces the chance of verbatim public code appearing while still allowing the assistant to suggest original code where no exact match is found.

The duplication detector blocks any code snippet that exceeds eight lines to avoid copyright issues is incorrect because the filter is not based on a fixed line count. It focuses on whether a suggestion is an exact duplicate of public code rather than how many lines it contains.

The duplication filter is enforced only for GitHub Copilot Business and Enterprise so organizations can maintain intellectual property compliance is incorrect because the filter is available beyond those tiers. Individuals can enable it in their settings and organizations on higher tiers can enforce it through policy, which means it is not exclusive to Business or Enterprise.

The duplication filter guarantees that Copilot suggestions never resemble any public code even partially is incorrect because the filter only suppresses exact matches. Suggestions that are similar or partially overlapping with public code can still appear and there is no absolute guarantee of novelty.

The correct choice is Adopt GitHub Copilot for Business to gain centralized management advanced privacy options and full support for private repositories.

This plan is designed for organizations that need stronger privacy protections and centralized control. Administrators can manage seats and policies across teams and they can enforce privacy features such as filtering suggestions that match public code while ensuring organization wide governance for private repositories. These capabilities align with the need for central policy enforcement and fine grained permission management.

Choose GitHub Copilot Free Plan to minimize costs while continuing to use Copilot in both public and private repositories is incorrect because it lacks organization level administration and policy controls and it does not provide the privacy protections required for proprietary code in private repositories.

Select GitHub Copilot Enterprise to integrate with GitHub Enterprise Cloud and enable enterprise governance and knowledge features is not the best fit for this scenario because it targets organizations that need enterprise cloud integration and advanced knowledge features. The requirements are fully met by the business tier without the added scope of enterprise capabilities.

Keep GitHub Copilot for Individuals because it already works with private repositories and offers enough security for sensitive code is incorrect because the individual plan lacks centralized management, policy enforcement, and team wide permission controls that are necessary for mission critical private code.

The correct answer is Use GitHub Copilot to draft comments and READMEs then have developers revise and tailor the wording for the codebase and domain.

This workflow uses Copilot to quickly produce first drafts while relying on engineers to validate accuracy, inject domain language, and ensure the documentation matches the architecture and coding conventions. Human editing reduces hallucinations and vague language, adds examples and links that reflect the repository, and keeps docs maintainable as services evolve. It also supports quality, security, and compliance reviews that automated tools cannot guarantee on their own.

Set up Cloud Build to auto-generate documentation from source and treat that output as complete without human review is incorrect because generated docs still require validation and contextualization, and this option removes the necessary human oversight. It also does not address inline comments and docstrings where Copilot can help the most.

Turn off Copilot for docs and require all documentation to be written by hand to ensure quality is incorrect because it throws away the speed and coverage benefits of assisted drafting without providing any assurance of higher quality. The best results come from editing AI drafts rather than forbidding them.

Allow Copilot to produce full documentation for each file and skip any manual edits to save time is incorrect because unreviewed AI output can be inaccurate, inconsistent with project terminology, and quickly become stale, which undermines trust and maintainability.

The correct option is Include precise values and constraints in the prompt such as “use 12% VAT and apply a 7% coupon”.

Providing explicit numbers and clear constraints strongly reduces ambiguity and signals that the discount calculation is required and how it should be applied. This makes it much more likely that Copilot will include the coupon logic rather than only computing tax, because the model is guided toward the exact steps and parameters it must implement.

State the language explicitly in the prompt such as “Write a Python function that computes an invoice total including VAT and a coupon” is not the most effective adjustment here. Declaring the language can be helpful for syntax and style but it does not add the missing specificity that drives inclusion of the discount calculation, especially since the original instruction already mentioned a coupon.

Paste several input and output examples of test cases into the prompt can help in some scenarios, yet it is less direct for this case. Without explicit constraints that highlight the coupon percentage and its application, examples might still not force the model to implement the discount logic, and they add unnecessary length compared to a concise constraint.

Shorten the request to “Function to compute cart total” removes important requirements and will make it even less likely that Copilot includes the coupon handling, because the instruction becomes more ambiguous and underspecified.

The correct option is gh copilot suggest.

This command opens an interactive experience in your terminal where you describe a goal in natural language and it proposes shell commands to achieve it. You can review what it plans to run and accept or edit the suggestion before execution which makes it well suited for step by step guidance.

The option gh copilot deploy is incorrect because the Copilot CLI does not include a deploy subcommand and deployment is not part of its terminal assistance features.

The option gh repo clone is incorrect because it clones a repository and it does not provide interactive suggestions in the terminal.

The option gh copilot refactor is incorrect because it is not a valid Copilot CLI subcommand and refactoring is not offered as an interactive terminal feature by that name.