Free IBM A1000-075: Foundations of AI Practice Test

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

A retail company wants to use IBM Watson to analyze customer feedback from emails, social media, and chat transcripts to understand customer sentiment. Which IBM Watson service would be MOST appropriate for this use case?

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

Which of the following is a key principle of responsible AI that addresses the concern of AI systems making decisions that humans cannot understand?

What type of neural network architecture is specifically designed to process sequential data such as time series or natural language?

A financial services company is developing an AI system to approve loan applications. What ethical concern should be the PRIMARY focus when designing this system?

Which IBM Watson service would a healthcare organization use to extract and structure information from unstructured medical documents such as clinical notes and research papers?

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

In the context of AI, what does the term 'bias' primarily refer to?

A company wants to build a virtual assistant that can handle customer service inquiries through natural conversation. Which IBM Watson service should they primarily use?

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

An organization is implementing an AI governance framework. Which element is MOST critical for ensuring ongoing compliance and responsible AI use?

What is 'transfer learning' in the context of deep learning?

Which IBM Watson service would be MOST appropriate for converting audio recordings of customer service calls into text for further analysis?

In reinforcement learning, what is the role of the 'reward signal'?

A global corporation is developing an AI system that will be deployed across multiple countries with different regulatory requirements regarding data privacy and AI transparency. What approach should they take to ensure compliance?

A data scientist notices that their classification model performs excellently on the training data (98% accuracy) but poorly on the test data (65% accuracy). They also observe that the model has a very complex architecture with many parameters. What is the MOST likely problem and appropriate solution?

An organization wants to use IBM Watson to build a custom model that can classify industry-specific documents into proprietary categories unique to their business. They have labeled training data. Which combination of IBM Watson services would be MOST appropriate?

What is the primary distinction between 'narrow AI' (weak AI) and 'general AI' (strong AI)?

A healthcare AI system trained primarily on data from one demographic group is being deployed to serve a diverse patient population. The system shows significantly lower accuracy for underrepresented groups. What is the BEST approach to address this issue?