Free IBM Assessment: Foundations of AI Practice Test

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

A retail company wants to implement a chatbot that can understand customer queries in natural language and provide relevant product recommendations. Which IBM Watson service would be most appropriate for understanding the intent of customer questions?

In supervised learning, what is the role of labeled data?

Which of the following scenarios represents the most significant ethical concern related to AI bias?

What is the primary purpose of Natural Language Processing (NLP) in AI systems?

A healthcare organization wants to analyze thousands of medical research papers to find relevant information about a rare disease. Which IBM Watson service would be most suitable for this use case?

What is the main difference between classification and regression in supervised learning?

A company developing an AI system for automated hiring decisions wants to ensure fairness and transparency. Which principle of responsible AI would require them to explain how the system makes decisions?

Which of the following best describes 'Deep Learning'?

A company wants to transcribe audio recordings from customer service calls into text for analysis. Which IBM Watson service should they use?

In the context of machine learning, what is 'overfitting'?

An AI system used in criminal justice for predicting recidivism rates shows different accuracy levels across different racial groups. What type of bias does this most likely represent?

What is the primary function of a neural network's activation function?

A global company needs to analyze customer feedback written in multiple languages and extract sentiment, emotions, and key topics. Which combination of IBM Watson services would be most effective?

What is the purpose of using a validation dataset during model training?

Which scenario best illustrates the principle of 'human-in-the-loop' AI design?

What distinguishes unsupervised learning from supervised learning?

A financial services company is building an AI-powered fraud detection system. During testing, they discover that the system flags a disproportionately high number of legitimate transactions from a specific geographic region as fraudulent. What is the most likely root cause and appropriate first step to address this issue?

In the context of AI systems, what does 'model explainability' refer to, and why is it particularly important in regulated industries?

A company wants to build a system that can identify and categorize different types of defects in manufactured products from images. They have a large dataset of product images but only a small subset is labeled with defect types. Which approach would be most practical?