GenAI Screen TestBy mfh.officials@gmail.com / January 7, 2025 Generative AI (Gen AI) Screening Test 1 / 20 What is "zero-shot learning" in Generative AI? Generating output for unseen tasks without specific training Removing overfitting during training Training models without any labeled data Reducing model training time to zero 2 / 20 What does "fine-tuning" mean in Generative AI? Increasing the number of layers in a model Adjusting a pre-trained model for specific tasks Training a new model from scratch Simplifying the dataset 3 / 20 Which of the following is NOT an example of Generative AI? Power BI ChatGPT DALL·E MidJourney 4 / 20 Which technique combines supervised and unsupervised learning for training Generative AI? Reinforcement learning Adversarial training Semi-supervised learning Zero-shot learning 5 / 20 What does "latent space" refer to in Generative AI? A memory buffer for AI systems A reduced-dimensional representation of input data A repository for training data A storage area for pre-trained models 6 / 20 What is a key component of a Generative Adversarial Network (GAN)? A feature extractor and classifier A discriminator and a generator A trainer and a learner An encoder and a decoder 7 / 20 What is Generative AI primarily used for? Data analysis and visualization Monitoring system performance Creating new content, such as images, text, or music Extracting insights from structured data 8 / 20 Which of the following technologies powers ChatGPT? Long Short-Term Memory (LSTM) Reinforcement Learning Convolutional Networks Transformers 9 / 20 What does the "T" in GPT stand for? Translation Transformer Training Transfer 10 / 20 What is the main role of the "discriminator" in a GAN? To optimize the learning rate To classify real and fake data To generate synthetic data To reduce noise in the input 11 / 20 Which of the following frameworks is widely used for developing Generative AI models? All of the above PyTorch TensorFlow JAX 12 / 20 Which of the following is a real-world application of Generative AI? Predictive analytics Fraud detection Image synthesis and text generation Customer segmentation 13 / 20 Which loss function is commonly used in training GANs? Binary Cross-Entropy Loss Cross-Entropy Loss Mean Squared Error (MSE) Hinge Loss 14 / 20 What is the purpose of "diffusion models" in Generative AI? To generate high-quality images by reversing noise To reduce training time To optimize hyperparameters To classify data 15 / 20 What is a common use case for Variational Autoencoders (VAEs)? Dimensionality reduction and generative tasks Predictive modeling Decision tree optimization Classification tasks 16 / 20 What is the main advantage of pre-trained Generative AI models? They eliminate the need for labeled data They generalize better to new tasks They reduce computational resources needed for fine-tuning They require no further training 17 / 20 What is the role of attention mechanisms in transformer-based models? To focus on relevant parts of the input sequence To simplify architecture design To reduce the model size To increase training speed 18 / 20 What is "prompt engineering" in the context of Generative AI? Optimizing system performance Tuning hyperparameters for training Designing the neural network architecture Crafting inputs to guide AI output 19 / 20 Which neural network architecture is commonly used in Generative AI? Decision Trees Recurrent Neural Networks (RNNs) Generative Adversarial Networks (GANs) Convolutional Neural Networks (CNNs) 20 / 20 Which of the following is a major ethical concern with Generative AI? Lack of scalability Biased or harmful content generation High computational cost Limited use cases Your score isThe average score is 60% 0% Restart quiz
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