Microsoft GH-300 GitHub Copilot Exam Exam Practice Test

GitHub Copilot can generate all types of unit tests, including those for edge cases and complex integration scenarios.

GitHub Copilot primarily suggests basic unit tests that focus on core functionalities, often requiring additional input from developers for comprehensive coverage.

GitHub Copilot can handle any complexity in code and automatically generate appropriate unit tests.

GitHub Copilot's limitations in generating unit tests can vary based on the IDE version you are using.

Limited training data

No biases in code suggestions

Ability to handle complex code structures

Extensive support for all programming languages

By engaging in real-time collaboration with multiple developers to write boilerplate code.

By predicting future coding requirements and pre-emptively generating boilerplate code.

By refactoring the entire codebase to eliminate boilerplate code without developer input.

By suggesting code snippets that can be reused across different parts of the project.

Code quality

Compatibility with user-specific settings

Suggestions matching public code (optional based on settings)

Performance benchmarking

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

Using well-known coding practices in your repository.

Ensuring that the correct context is available to GitHub Copilot.

Close all the tabs in your IDE that do not have tests in them.

Having a high test coverage percentage in the codebase.

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

By regularly updating the AI filtering algorithm.

By relying on a well-regarded AI development company.

By focusing on user satisfaction with the content filtering.

By providing clear explanations about the types of content the AI is designed to filter and how it arrives at its conclusion.

Create a technical requirement specification from the business requirement documentation

Explain a legacy COBOL code and translate the code to another language like Python.

Creation of a unit test scenario for newly developed Python code

Creation of end-to-end performance testing scenarios for a web application

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.

Add teams to the Copilot subscription for an organization

Upgrade or downgrade the subscription tier

Migrate Copilot seat assignments between GitHub organizations

Reassign Copilot seats based on GitHub repository size

Remove teams from the Copilot subscription for an organization

By filling out a feedback form on the GitHub website

By emailing the support team directly

By posting on the GitHub forums

Through the "Share Feedback" button in the Copilot Chat panel

Prompt data is stored unencrypted for faster processing.

Prompt data is used internally by GitHub for improving the search engine.

Prompt data is used to train machine learning models for better code suggestions.

Real-time user input helps generate context-aware code suggestions.

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.

All repos are indexed

Indexing is static

It is an Enterprise feature

All file types are indexed

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.

It filters out irrelevant information from the user's input prompt.

It enriches the input prompt with additional context before passing it to the language model.

It directly generates a response based on the user's input prompt.

It formats the output response before presenting it to the user.

GitHub Copilot Individual

GitHub Copilot Business

GitHub Copilot Business for non-GHE Customers

GitHub Copilot Enterprise

GitHub Copilot Teams

Initiates a code review with static analysis tools for security and logic errors.

Converts pseudocode into executable code, optimizing for readability and maintainability.

Generates new code snippets based on language syntax and best practices.

Proposes changes for detected issues, suggesting corrections for syntax errors and programming mistakes.

The Gen AI tool is overloaded with too many requests at once.

The Gen AI tool is experiencing downtime and is not fully recovered.

The Gen AI tool is programmed with a focus on creativity over factual accuracy.

The training data might contain biases or inconsistencies.

By running code linters and formatters.

By providing visual diagrams of the code structure.

By answering questions about the code and generating explanations.

By automatically refactoring code to improve readability.

GitHub Copilot can directly convert legacy applications into cloud-native architectures.

GitHub Copilot can suggest modern programming patterns based on your code.

GitHub Copilot can create and deploy full-stack applications based on a single query.

GitHub Copilot can refactor applications to align with upcoming standards.

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

By opening the relevant tabs in your IDE

By adding relevant code snippets to your prompt

By adding the important files to your .gitconfig

By adding the full file paths to your prompt of important files

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.

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 using a specific setting in GitHub Copilot's configuration.

By using a specific command in the terminal.

By using specific prompt examples in your chat request.

By using a specific slash command in the prompt.

Explain code and suggest improvements

Generate unit tests

Plan coding tasks

Find out about releases and commits

"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."

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.

If GitHub Copilot can connect to the server selected in your user settings.

If the user is part of the content exclusion team that limits the use of content exclusions.

If the content exclusion settings changed in the last 30 minutes or before that.

If the user is in an organization that has content exclusions configured.

GitHub Copilot can fix syntax errors without user input.

GitHub Copilot can automatically rewrite code to follow best practices.

GitHub Copilot can suggest refactoring improvements for better code quality.

GitHub Copilot can remove unnecessary files from the project directory.

Blocking public code matches

Blocking license check configuration

Allowing public code matches

Allowing license check configuration

The explanations are dynamically updated based on user feedback.

Reviewing and validating the generated output for accuracy and completeness.

GitHub Copilot Chat uses a static database for generating explanations.

The explanations are primarily derived from user-provided documentation.

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

To compare user-generated code against a private repository for potential matches.

To allow administrators to control which suggestions are visible to developers based on custom criteria.

To encourage the user to follow coding best practices preventing code duplication.

To detect and block suggestions that match public code snippets on GitHub if they contain about 150 characters.