Artificial Intelligence Quiz 2

How can bias in the training data affect the predictions of a machine learning model?

• It can make the predictions more accurate.

• It has no effect on the predictions.

• It can lead to skewed or unfair predictions.

• It can convert supervised learning into unsupervised learning.

What is a benefit of using AI applications?

• Increased computational errors

• Reduced human effort in tasks

• Higher employment costs

• Decreased efficiency

Which of these is NOT a common approach to machine learning?

• Reinforcement learning

• Supervised learning

• Unsupervised learning

• Directive learning

What issue can arise from using AI applications?

• Enhanced creativity and innovation

• Unemployment due to automation

• Increased human interaction

• Improved data security

What is the primary difference between ‘data-driven’ and ‘rule-based’ approaches to application development?

• Data-driven relies on historical data, while rule-based relies on predefined rules.

• Data-driven uses physical models, while rule-based uses digital models.

• Data-driven requires human intervention, while rule-based is fully automated.

• There is no difference; both terms mean the same thing.

Why is it necessary to have both training and test data in machine learning?

• To increase the quantity of data available

• To ensure the model can generalize beyond the training data

• To make the training process faster

• To make the model more complex

How is machine learning related to artificial intelligence?

• Machine learning is a subset of artificial intelligence.

• Machine learning is the same as artificial intelligence.

• Machine learning competes with artificial intelligence.

• Machine learning and artificial intelligence are unrelated.

Which of the following is an example of an AI application?

• Spreadsheet software

• Text editor

• Voice-activated assistant

• Web browser

In supervised learning, how is classification typically solved?

• By finding a best-fit line in data

• By using labeled data to train the model

• By creating a rule-based system manually

• By allowing the model to label data on its own