Free IBM A1000-108 - Assessment: Foundations of AI and Machine LearningPractice Test

What is the primary difference between Artificial Intelligence (AI) and Machine Learning (ML)?

A retail company wants to predict customer purchase behavior based on historical transaction data. Which type of machine learning approach is most appropriate for this scenario?

Which of the following best describes the concept of 'bias' in AI ethics?

During data preprocessing, a dataset contains a numerical feature 'income' with values ranging from $15,000 to $500,000, and another feature 'age' ranging from 18 to 65. Why is feature scaling important before training a machine learning model?

What is the primary purpose of a confusion matrix in machine learning model evaluation?

A data scientist discovers that their training dataset has 95% samples from Class A and only 5% from Class B. What problem does this represent, and what is a common technique to address it?

Which statement best describes the relationship between Deep Learning and Neural Networks?

An organization is implementing an AI system for loan approval decisions. According to responsible AI principles, what is the most important consideration they should address?

What is the primary purpose of splitting data into training, validation, and test sets?

In the context of Natural Language Processing (NLP), what is the purpose of tokenization?

A machine learning model performs exceptionally well on training data (98% accuracy) but poorly on test data (65% accuracy). What problem is this, and what is the most appropriate solution?

Which technique is most appropriate for handling missing values in a dataset when the missing data follows a pattern related to other variables?

What distinguishes supervised learning from unsupervised learning?

A global company is deploying an AI-powered hiring system. What potential ethical concern should they prioritize addressing to ensure responsible AI practices?

In computer vision, what is transfer learning and why is it beneficial?

What is the primary difference between precision and recall in classification model evaluation?

Which of the following best describes the 'explainability' principle in responsible AI?

A data preprocessing pipeline needs to convert categorical variables like 'color' (red, blue, green) into a format suitable for machine learning algorithms. Which encoding technique should be used for nominal categorical variables with no ordinal relationship?

What type of AI system is IBM Watson primarily known as?

A financial institution is implementing an AI model for fraud detection where missing a fraudulent transaction (false negative) is much more costly than flagging a legitimate transaction as fraud (false positive). Which evaluation metric should they prioritize when optimizing the model?