Microsoft GH-300 GitHub Copilot Exam Exam Practice Test

GitHub Copilot automatically updates deprecated features in its suggestions to the latest version.

GitHub Copilot may suggest deprecated syntax or features if they are present in its training data.

GitHub Copilot always filters out deprecated elements to promote the use of current standards.

GitHub Copilot rejects all prompts involving deprecated features to avoid compilation errors.

The GitHub Copilot icon in the status bar of the editor will display a message

The GitHub Copilot logs on GitHub.com under your user settings

The code suggestions window will display a warning message

The GitHub Copilot errors panel in your IDE

Up to 30 minutes

45 - 60 minutes

60 - 90 minutes

24 hours

Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)

Sexually suggestive or explicit content

Code that contains logical errors or produces unexpected results

Code comments containing strong opinions or criticisms

Information from the IDE like open tabs, cursor location, selected code.

All the code in the current repository and any git submodules.

The open tabs in the IDE and the current folder of the terminal.

All the code visible in the current IDE.

"Show me how to improve the readability of this function."

"Suggest ways to enhance the maintainability of this code segment."

"Refactor my application to meet the latest coding standards."

"Predict future coding trends and update my codebase accordingly."

Suggestions matching public code to 'blocked'

Enforce blocking of MIT or GPL licensed code

You need to check code suggestions yourself before accepting

Enable GitHub Copilot license checking

Navigate to the Security tab in the organization's GitHub settings

Navigate to the Insights tab in the repository settings

Use the Audit log section in the organization's GitHub settings

Use the Code tab in the GitHub repository

The default execution confirmation

Usage analytics

The default editor

GitHub CLI subcommands

Limited training data

No biases in code suggestions

Ability to handle complex code structures

Extensive support for all programming languages

Telling GitHub Copilot about the mechanism you want it to use and how to incorporate that into the response

Telling GitHub Copilot from which sources it should base the response on

Telling GitHub Copilot to try multiple times to answer the prompt

Telling GitHub Copilot to iterate several times on the answer before returning it to you

Improve on the computational efficiency and speed.

Focus on accuracy of the data.

Collect massive amounts of data for training.

Use diverse data, fairness metrics, and human oversight.

Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.

Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.

Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.

Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.

Apply policies through the GitHub Actions configuration

Create a copilot.policy file in each repository

Configure the policies in the organization settings

Create a copilot.policy in the .github repository

AI algorithms are incapable of perpetuating existing biases.

AI systems can sometimes make decisions that are difficult to interpret.

AI eliminates the need for data privacy regulations.

AI replaces the need for developer opportunities in most fields.

Optimize the usage of memory-intensive operations within generated code

Apply prompt engineering techniques to be more specific

Use a single GitHub Copilot Chat query to find resolutions for the collection of technical requirements

Limit the number of concurrent users accessing GitHub Copilot Chat

Type out the code snippet → Use the copilot refine command to enhance it → Review the suggested command.

Write code comments → Press the suggestion shortcut → Select the best suggestion from the list.

Use gh copilot suggest → Write the command you want → Select the best suggestion from the list.

Describe the project's architecture → Use the copilot generate command → Accept the generated suggestion.

GitHub Copilot Individuals

GitHub Copilot Business

GitHub Copilot Enterprise

GitHub Copilot Codespace

Get a summary of GitHub Copilot usage for organization members

List all GitHub Copilot seat assignments for an organization

View code suggestions for a specific user

List of all unsubscribed GitHub Copilot members within an organization

Utilizing GitHub Copilot's ability to create fictitious information from patterns in training data.

Leveraging GitHub Copilot's ability to independently initiate and manage data storage services.

Utilize GitHub Copilot's capability to directly access and use databases to create sample data.

Leveraging GitHub Copilot's suggestions to create data based on API documentation in the repository.

By analyzing the codebase and suggesting more efficient algorithms or data structures.

By automatically rewriting the codebase to use more efficient code.

By enforcing strict coding standards that ensure optimal performance.

By providing detailed reports on the performance of the codebase.

Use the emojis in the Copilot Chat interface.

The feedback section on the GitHub website.

By tweeting at GitHub's official X (Twitter) account.

The Settings menu in the GitHub Mobile app.

It primarily relies on the model's training data to generate responses.

It primarily uses search results from Bing to generate responses.

It combines its training data set, code in user repositories, and external sources like Bing to generate responses.

It uses user-provided documentation exclusively to generate responses.

Running code suggestions through filters designed to detect public code

Comparing suggestions against public code using machine learning.

Analyzing the context and structure of the code being written

Reviewing the user's browsing history to identify public repositories

The API can generate detailed reports on code quality improvements made by GitHub Copilot.

The API can track the acceptance rate of code suggestions accepted and used in the organization.

The API can refactor your code to improve productivity.

The API can provide feedback on coding style and standards compliance.

The API can provide Copilot Chat specific suggestions acceptance metrics.

Because GitHub Copilot can cover all possible scenarios in your test cases.

Because GitHub Copilot generates the best code possible for the test scenario.

Because GitHub Copilot's generated test cases may not cover all possible scenarios.

Because GitHub Copilot replaces the need for manual testing.

Enterprise settings → Copilot → Policies

Repository settings → Copilot → Policies

Organization settings → Copilot → Policies

User settings → Copilot → Policies

GitHub Copilot can automatically handle project management tasks.

GitHub Copilot can completely replace the need for human collaboration.

GitHub Copilot can predict and prevent bugs before they occur.

GitHub Copilot allows developers to stay in their IDE.

GitHub Copilot may not always produce optimized or best-practice code for refactoring.

GitHub Copilot can only be used with a limited set of programming languages.

GitHub Copilot always produces bug-free code during refactoring.

GitHub Copilot understands the context of your entire project and refactors code accordingly.

GitHub Copilot Individual

GitHub Copilot Business

GitHub Copilot Business for non-GHE Customers

GitHub Copilot Enterprise

GitHub Copilot Teams

Accepted chat suggestions

Code suggestions made by GitHub Copilot

Changes to content exclusion settings

Suggestions blocked by duplication detection filtering

A project plan for the team needs to be generated using a project management software.

Create a documentation file for the newly created code base.

A developer is added to a new project and would like to understand the current software code.

Fast tracking of release management activities to move code to production main branch.

Using machine learning models trained only on private repositories

Reviewing and storing user-specific private repository data for future suggestions

Filtering out suggestions that match code from public repositories

Implementing safeguards to detect and avoid suggesting verbatim snippets from public code

By automatically generating detailed requirements documents.

By providing templates and code snippets that help in documenting requirements.

By identifying and fixing potential requirement conflicts when using /help.

By managing stakeholder communication and meetings.

You are working in files included in your .gitignore

You do not have an active internet connection

Your programming language is not supported

Your content exclusion is active and blocks the use of GitHub Copilot

You do not have a valid GitHub Copilot license

Avoid using Copilot for complex tasks.

Disable Copilot's inline suggestions.

Regularly review and refactor the code suggested by Copilot.

Rely solely on Copilot's suggestions without reviewing them.