Free IBM A1000-119Practice Test

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

Which of the following best describes supervised learning?

A retail company wants to group customers with similar purchasing behaviors without predefined categories. Which type of machine learning approach should they use?

What is a key ethical concern when deploying AI systems that make decisions affecting individuals?

Which term describes an AI system's ability to provide understandable reasoning for its decisions?

A healthcare organization is building a diagnostic AI system. During testing, they discover the model performs poorly on data from certain demographic groups. What is the most likely cause?

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

A financial services company wants to detect fraudulent transactions in real-time. The fraudulent transactions represent only 0.1% of all transactions. What challenge does this scenario present?

Which of the following is an example of Natural Language Processing (NLP) application?

What is the purpose of establishing an AI governance framework in an organization?

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

A manufacturing company wants to use AI to predict when equipment will fail so they can perform maintenance before breakdowns occur. What type of AI application is this?

What does the term 'overfitting' mean in machine learning?

Which principle should guide the collection and use of data in AI systems to ensure privacy protection?

What distinguishes deep learning from traditional machine learning approaches?

An e-commerce company has deployed a recommendation system that consistently suggests products primarily purchased by one demographic group to all users, regardless of their actual preferences. What AI fairness issue does this represent, and what should be the first step to address it?

A data scientist notices that their regression model achieves 98% accuracy on the training set but only 65% on the test set. They also observe that the model has learned very specific patterns including noise. What is the best approach to address this issue?

A multinational corporation is implementing an AI-powered hiring system that will screen job applications across multiple countries with different regulatory requirements. Which approach best ensures compliance and ethical deployment?

A computer vision system is being developed to identify defects in manufactured products on an assembly line. The system needs to process images in real-time and adapt to new defect types without complete retraining. Which combination of approaches would be most effective?

In a production machine learning pipeline, data drift is detected where the statistical properties of input features have changed significantly from the training data distribution. Model performance has degraded. What is the most comprehensive strategy to address this issue?