USAII CAIC Dumps - Pass the Certified Artificial Intelligence Consultant Exam in 2026

The correct answer is B. House price prediction. Supervised learning is a machine learning approach where a model is trained using labeled data. In a house price prediction problem, the training data usually contains property features such as size, location, number of rooms, age of the house, and past selling prices. The known selling price acts as the label or target value. The model learns the relationship between the input features and the price, then predicts prices for new houses.

A driverless car is not the best single example because autonomous driving uses a combination of AI techniques, including supervised learning, reinforcement learning, computer vision, sensor fusion, planning, and control systems. ChatGPT is a generative AI language model and is not typically used as the basic example of supervised learning in this context. Since house price prediction directly represents supervised learning with labeled input-output data, the correct answer is B.

The correct answer is E. a, b and c only because respect for human autonomy, prevention of harm, and explicability are all recognized ethical principles in responsible AI. Respect for human autonomy means AI systems should support human decision-making rather than unfairly manipulate, replace, or override people in ways that remove meaningful human control. This is especially important in business, healthcare, finance, hiring, and other high-impact AI use cases.

Prevention of harm is also a core ethical principle because AI systems should be designed and deployed to reduce physical, psychological, financial, social, operational, and reputational risks. Organizations must consider safety, reliability, misuse prevention, bias reduction, and risk controls.

Explicability is correct because AI decisions should be understandable, explainable, and auditable where appropriate. Stakeholders should be able to understand how and why an AI system produces important outputs. Since all three listed items are valid ethical principles, the correct answer is E. a, b and c only.

The correct answer is E. All of the above because deep learning, expert systems, natural language processing, and robotics are all connected with AI applications that support or model intelligent behavior. Cognitive modeling in AI is concerned with building systems that can represent, simulate, or support human-like capabilities such as learning, reasoning, decision-making, perception, language understanding, and action.

Deep learning is used to recognize patterns from large amounts of data and is common in speech recognition, image analysis, recommendation systems, and generative AI. Expert systems use knowledge bases and rules to support decision-making in specialized domains. Natural language processing helps AI systems understand, interpret, generate, and respond to human language. Robotics applies AI to physical systems so machines can sense, plan, move, and perform tasks in real-world environments.

Since all the listed options are valid AI application areas related to intelligent and cognitive capabilities, the correct answer is E. All of the above.

The correct answer is D. a and b only because few-shot learning is a machine learning technique that allows a model to learn or adapt to a new task using only a small number of labeled examples. It is especially useful when collecting large labeled datasets is expensive, slow, or difficult. Instead of requiring thousands or millions of labeled records, few-shot learning depends on prior knowledge learned by the model and applies that knowledge to new examples with limited supervision.

Statement A is correct because few-shot learning is recognized as a machine learning approach. Statement B is also correct because the core idea of few-shot learning is learning from very limited labeled data. Statement C is not correct because learning from unlabeled data is more closely associated with unsupervised learning or semi-supervised learning, not the standard definition of few-shot learning. Therefore, the correct answer is D. a and b only.

The correct answer is A. Weak AI. Artificial Narrow Intelligence, or ANI, is commonly called Weak AI because it is designed to perform a specific task or a limited set of tasks within a defined domain. Examples include recommendation engines, search engines, spam filters, facial recognition systems, voice assistants, fraud detection tools, and chatbots. These systems can perform their assigned functions effectively, but they do not possess general intelligence, consciousness, self-awareness, or human-like understanding across all domains.

Strong AI and General AI refer to Artificial General Intelligence, which would be capable of broad reasoning, learning, and problem-solving across many tasks like a human. SuperAI refers to a theoretical level of intelligence beyond human capability. ExpertAI is not the standard expression for ANI. Since ANI is task-specific and limited in scope, it is correctly expressed as Weak AI.