Free IBM Assessment: Foundations of AIPractice Test

What is the primary difference between narrow AI and general AI?

A retail company wants to implement an AI system that can predict customer purchasing behavior based on historical transaction data. Which type of machine learning approach is most appropriate for this scenario?

Which of the following is a key principle of responsible AI that addresses the requirement for AI systems to provide understandable reasoning for their decisions?

In the context of neural networks, what is the primary purpose of an activation function?

A development team is building a chatbot application using IBM Watson Assistant. What is the primary purpose of defining 'intents' in this context?

What is the main difference between overfitting and underfitting in machine learning models?

An organization is concerned about bias in their AI-powered hiring system. Which approach would be most effective in identifying and mitigating bias?

Which technique is commonly used to evaluate the performance of a classification model and involves dividing the dataset into multiple subsets for training and validation?

A company wants to use AI to automatically categorize incoming customer support emails into different departments without having predefined categories. Which type of learning approach should they use?

In natural language processing (NLP), what is the purpose of tokenization?

A data scientist notices that their deep learning model's training loss continues to decrease while validation loss starts to increase after a certain number of epochs. What is the most appropriate action to address this issue?

An enterprise is developing an AI application that will process customer data. According to AI governance best practices, what should be established before deploying the system?

When developing a conversational AI application, what is the role of 'entities' in understanding user input?

Which IBM AI service would be most appropriate for analyzing the tone and emotion in customer feedback text?

A manufacturing company wants to implement an AI system that learns optimal machine settings by trying different configurations and receiving feedback on production quality. Which machine learning approach is most suitable?

What is the primary purpose of using a confusion matrix in evaluating a classification model?

In the context of AI development, what does the term 'model drift' refer to?

Which of the following scenarios best exemplifies the AI fairness principle?

A deep learning model is being trained on a dataset with highly imbalanced classes (95% negative, 5% positive examples). The model achieves 95% accuracy but fails to identify any positive cases. What metric would better evaluate this model's performance?

An organization wants to implement AI transparency practices. Which of the following actions best supports this goal?