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