Artificial Intelligence Gen AI Gen AI Quiz Quiz Gen AI Medium Level Quiz 4 Data AI Admin October 10, 2024 Welcome to the Generative AI Medium Level Quiz 4! 🤖This quiz challenges your understanding of advanced generative AI concepts, covering topics like diffusion models, autoregressive models, and transformers. Get ready to put your skills to the test. ⏰ Time’s Up!The quiz is now over, and your responses have been submitted. Let’s see how well you’ve mastered medium-level Generative AI concepts! Generative AI Medium Level Quiz 4 This is a medium-level quiz designed to assess your knowledge of Generative AI, including GANs, VAEs, diffusion models, and transformer architectures. The quiz contains 10 questions, some of which have multiple correct answers. Dive into these advanced concepts and see how well you grasp the intricacies of Generative AI 1 / 10 Which of the following describes the relationship between GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers)? GPT is autoregressive while BERT is bidirectional GPT uses the transformer encoder and BERT uses the transformer decoder BERT is used in sequence-to-sequence tasks, whereas GPT is used for sentiment analysis BERT generates text while GPT is used for text classification 2 / 10 Which of the following is a challenge specific to diffusion models in generative AI? Mode collapse Reverse noise sampling Slow sampling speed Difficulty in learning the latent space 3 / 10 Which of the following methods can improve the quality of text generated by a transformer model like GPT? Reducing the number of training epochs Using beam search instead of greedy decoding Adding positional encoding to the input Fine-tuning on a domain-specific dataset 4 / 10 In conditional GANs, the generator receives both random noise and the class label as inputs to condition the generated output. True False 5 / 10 Which of the following techniques can help stabilize the training of Generative Adversarial Networks (GANs)? Spectral normalization Batch normalization Gradient clipping Adding noise to the inputs 6 / 10 In transformer-based generative models, the self-attention mechanism helps the model attend to all tokens in the sequence simultaneously. False True 7 / 10 What is the role of the "latent space" in Variational Autoencoders (VAE)? It reduces overfitting by regularizing the model It generates labels for classification It represents a compressed, continuous space of the input data It helps in generating adversarial examples 8 / 10 What is the key advantage of using diffusion models over GANs for generative tasks? GANs can only generate text, not images Diffusion models use RNNs instead of CNNs Diffusion models produce higher resolution outputs Diffusion models avoid the mode collapse problem 9 / 10 Which of the following loss functions is commonly used in training Variational Autoencoders (VAEs)? Mean squared error (MSE) Cross-entropy loss Wasserstein loss Kullback–Leibler (KL) divergence 10 / 10 In autoregressive models, the next token is generated by conditioning only on the preceding token. True False Your score isThe average score is 0% 0% Restart quiz Share this… Whatsapp Linkedin Facebook Twitter Gmail Related Tags: Gen AI, Gen AI Quiz, Quiz Continue Reading Previous Gen AI Basics Quiz 3Next Deep Learning Medium Quiz 4 More Stories Artificial Intelligence AI Agent : A Personalized Chatbot Using LangGraph and LangChain balugorad April 8, 2025 Artificial Intelligence Deep Learning Gen AI Machine Learning Natural Language Processing Projects Technology DriveXpert AI Assistant : Users quickly solve their car-related queries balugorad January 15, 2025 Artificial Intelligence Open Source vs Paid Large Language Models (LLMs): A Strategic Comparison balugorad January 15, 2025 Leave a Reply Cancel replyYou must be logged in to post a comment.
