Free IBM A1000-078 - Assessment: Foundations of AIPractice Test

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

A retail company wants to predict customer churn based on historical purchase patterns, browsing behavior, and customer service interactions. Which type of machine learning approach is most appropriate?

Which IBM Watson service would be most appropriate for analyzing customer sentiment from social media posts and product reviews?

What is the purpose of a training dataset in machine learning?

A healthcare provider wants to implement an AI system to assist doctors in diagnosing diseases from medical images. What ethical consideration should be prioritized?

What distinguishes Deep Learning from traditional Machine Learning?

A company is building a chatbot to handle customer service inquiries. Which IBM Watson service is specifically designed for this purpose?

In the context of AI model development, what is 'overfitting' and why is it problematic?

What is Natural Language Processing (NLP) in the context of AI?

A financial institution is developing a credit scoring model. During testing, they discover the model performs significantly worse for certain demographic groups. What AI principle is being violated?

Which scenario best describes a use case for reinforcement learning?

A company wants to extract insights from thousands of PDF documents, including contracts, reports, and emails. Which IBM Watson service would be most suitable?

What is the primary purpose of the validation dataset in machine learning model development?

In a smart manufacturing environment, AI is being used to predict equipment failures before they occur. What type of AI application is this?

What is the role of labeled data in supervised learning?

A global company needs to provide customer support in multiple languages. Their current chatbot only works in English. Which combination of IBM Watson services would best address this requirement?

What is 'bias' in the context of AI and machine learning models?

An e-commerce company wants to recommend products to users based on their browsing history and purchases, without any explicit ratings. They also want to discover hidden patterns in customer behavior. Which machine learning approach is most appropriate?

Which statement best describes the concept of 'AI transparency' or 'explainability'?

A data science team is experiencing significant accuracy differences between their model's performance on the training set (95% accuracy) and the test set (65% accuracy). What is the most likely problem and appropriate solution?