A company is developing a GitHub Copilot extension that generates code for a loan approval system. The system uses historical data that contains biases against certain groups. The company wants to ensure fairness while also maintaining the system's accuracy. What is the most effective approach?

Use a fairness-aware algorithm that adjusts for bias.

Rely on human reviewers to catch biased decisions.

Remove all sensitive attributes from the training data.

Use the system as-is because it is trained on historical data.

Which of the following is a common risk associated with generative AI outputs?

The model may generate content that is factually incorrect but appears convincing.

The model cannot leak sensitive information because it is trained on public data only.

The model always provides accurate and verified information.

The model is immune to bias because it is trained on diverse data.

When a team uses a generative AI tool to create customer-facing content, what is a responsible practice regarding AI involvement?

Only use AI for internal documents, never for customer-facing content.

Clearly communicate to customers that AI was used to generate the content.

Keep the use of AI secret to avoid customer concerns.

Use AI-generated content without any review because it is usually accurate.

A company is using GitHub Copilot to generate code for a social media platform. The code sometimes suggests content that could be considered offensive or biased. The company wants to prevent this while not over-restricting the tool's usefulness. What is the most effective strategy?

Use a diverse dataset and regularly audit the model for biased outputs.

Rely on user reports to identify and fix biased suggestions.

Disable Copilot for content generation tasks.

Implement a content filter that blocks any potentially offensive suggestions.

Which practice is essential when using generative AI to ensure that outputs are appropriate for the intended use?

Using the AI without informing users that AI was involved

Maintaining human oversight of the AI's outputs

Disabling all safety features to improve performance

Relying solely on the AI's output without review

GitHubCopilot Certification

Domain 1Objective 1

Understand Responsible AI Principles GH-300 Practice Questions (Page 4)

Part of the Use GitHub Copilot responsibly domain, which accounts for 15-20% of the GH-300 exam.

28questions here

6free pages

6concepts

15-20%of the exam

Questions 16–20

16expert · hard
A company is developing a GitHub Copilot extension that generates code for a loan approval system. The system uses historical data that contains biases against certain groups. The company wants to ensure fairness while also maintaining the system's accuracy. What is the most effective approach?
Select an answer first
17foundation · easy
Which of the following is a common risk associated with generative AI outputs?
Select an answer first
18foundation · easy
When a team uses a generative AI tool to create customer-facing content, what is a responsible practice regarding AI involvement?
Select an answer first
19expert · hard
A company is using GitHub Copilot to generate code for a social media platform. The code sometimes suggests content that could be considered offensive or biased. The company wants to prevent this while not over-restricting the tool's usefulness. What is the most effective strategy?
Select an answer first
20foundation · easy
Which practice is essential when using generative AI to ensure that outputs are appropriate for the intended use?
Select an answer first

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