Final Quiz

Congratulations on reaching the final module of the Artificial Intelligence course. This quiz tests your understanding of AI ethics, safety, and the future of AI—and celebrates what you have achieved.

12 min•By Priygop Team•Updated 2026

Final Quiz

Question 1 of 5

What is AI hallucination?

What You Have Achieved

Module 1: What AI is, its history, the difference between narrow and general AI, and real-world examples
Module 2: How AI learns from data: features, labels, training data, testing data, patterns, and predictions
Module 3: Machine Learning types (supervised, unsupervised, reinforcement) and Deep Learning
Module 4: Neural networks: neurons, weights, bias, activation functions, forward pass, loss, and gradient descent
Module 5: Deep learning: layers, epochs, batch size, learning rate, overfitting and underfitting
Module 6: Computer Vision: pixels, CNNs, image classification, object detection, face recognition
Module 7: Natural Language Processing: tokenization, sentiment analysis, chatbots, translation
Module 8: Generative AI: text generation, image generation, audio, video, and limitations
Module 9: Large Language Models: tokens, context windows, hallucination, training stages
Module 10: AI in the real world: healthcare, banking, education, transportation, and more
Module 11: Building an AI application: data, features, training, evaluation, and deployment
Module 12: AI ethics, safety, and the future: responsible AI, bias, privacy, hallucinations, security, oversight, and career paths

Diagram

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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence

Key Takeaways

Congratulations on reaching the final module of the Artificial Intelligence course.
Module 1: What AI is, its history, the difference between narrow and general AI, and real-world examples
Module 2: How AI learns from data: features, labels, training data, testing data, patterns, and predictions
Module 3: Machine Learning types (supervised, unsupervised, reinforcement) and Deep Learning

Back to Course

Topics in This Module

Final Quiz

Question 1 of 5

What is AI hallucination?

What You Have Achieved

Module 1: What AI is, its history, the difference between narrow and general AI, and real-world examples

Module 2: How AI learns from data: features, labels, training data, testing data, patterns, and predictions

Module 3: Machine Learning types (supervised, unsupervised, reinforcement) and Deep Learning

Module 4: Neural networks: neurons, weights, bias, activation functions, forward pass, loss, and gradient descent

Module 5: Deep learning: layers, epochs, batch size, learning rate, overfitting and underfitting

Module 6: Computer Vision: pixels, CNNs, image classification, object detection, face recognition

Module 7: Natural Language Processing: tokenization, sentiment analysis, chatbots, translation

Module 8: Generative AI: text generation, image generation, audio, video, and limitations

Module 9: Large Language Models: tokens, context windows, hallucination, training stages

Module 10: AI in the real world: healthcare, banking, education, transportation, and more

Module 11: Building an AI application: data, features, training, evaluation, and deployment

Module 12: AI ethics, safety, and the future: responsible AI, bias, privacy, hallucinations, security, oversight, and career paths

Diagram

Loading diagram…

Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence

Topics in This Module