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 What is the key advantage of using diffusion models over GANs for generative tasks? Diffusion models produce higher resolution outputs Diffusion models use RNNs instead of CNNs Diffusion models avoid the mode collapse problem GANs can only generate text, not images 2 / 10 Which of the following methods can improve the quality of text generated by a transformer model like GPT? Fine-tuning on a domain-specific dataset Adding positional encoding to the input Reducing the number of training epochs Using beam search instead of greedy decoding 3 / 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 4 / 10 In autoregressive models, the next token is generated by conditioning only on the preceding token. True False 5 / 10 Which of the following is a challenge specific to diffusion models in generative AI? Reverse noise sampling Difficulty in learning the latent space Mode collapse Slow sampling speed 6 / 10 Which of the following loss functions is commonly used in training Variational Autoencoders (VAEs)? Mean squared error (MSE) Wasserstein loss Cross-entropy loss Kullback–Leibler (KL) divergence 7 / 10 In conditional GANs, the generator receives both random noise and the class label as inputs to condition the generated output. False True 8 / 10 Which of the following describes the relationship between GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers)? GPT uses the transformer encoder and BERT uses the transformer decoder BERT generates text while GPT is used for text classification BERT is used in sequence-to-sequence tasks, whereas GPT is used for sentiment analysis GPT is autoregressive while BERT is bidirectional 9 / 10 In transformer-based generative models, the self-attention mechanism helps the model attend to all tokens in the sequence simultaneously. True False 10 / 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 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.
