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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
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NEW QUESTION # 16
Consider the following PyTorch code snippet for a GAN discriminator:
Answer: D
Explanation:
The code calculates the hinge loss. The loss for real samples is - , which penalizes the discriminator when the output for real samples is less than 1. The loss for fake samples is + fake_output))' , which penalizes the discriminator when the output for fake samples is greater than -1. The 'torch.mean' function calculates the mean over all elements of the input tensor, so the 'dim' argument is not needed.
NEW QUESTION # 17
You're tasked with building a system that generates personalized exercise recommendations based on user's text descriptions of their fitness goals and images of their current physical condition. Due to privacy concerns, you cannot directly access the user's raw images or text after initial processing. What technique can allow you to continue to train the model while respecting these privacy constraints?.
Answer: E
Explanation:
Federated learning allows training a model across multiple decentralized devices or servers holding local data samples, without exchanging them. This is perfect for privacy-sensitive scenarios as the raw data remains on the user's device. Transfer learning relies on pre- trained models, data augmentation modifies existing data, GANs generate new data (but still require initial data access), and reinforcement learning optimizes actions through interaction with an environment.
NEW QUESTION # 18
You are training a Variational Autoencoder (VAE) and notice that the generated samples are blurry and lack detail. Which of the following adjustments could help improve the quality and sharpness of the generated images2 Select all that apply.
Answer: A,B,D,E
Explanation:
Increasing network capacity allows the model to learn more complex representations. Decreasing the KL divergence allows the decoder to focus more on reconstruction, potentially sacrificing some disentanglement. Increasing the latent space provides more room for capturing variations in the data. Using a more powerful decoder helps in generating sharper images
NEW QUESTION # 19
Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?
Answer: B
Explanation:
A GAN consists of two networks trained in an adversarial minimax game: a generator that learns to produce synthetic samples from random noise, and a discriminator that learns to distinguish those generated samples from real training data. As training progresses, the generator improves at producing increasingly realistic samples in an attempt to fool the discriminator, while the discriminator improves at detecting fakes - at convergence (ideally), the generator produces samples statistically indistinguishable from the real training distribution. This generative objective - producing new, realistic data resembling the training distribution - is GAN's defining purpose, applied to images, audio, tabular data, and other domains.
The remaining options describe fundamentally different task categories that GANs are not designed for:
decision optimization based on historical data (B) describes reinforcement learning or classical decision- theoretic optimization, not generative modeling. Classification and categorization (C) is a discriminative task
- GANs' discriminator component performs a real-vs-fake discrimination internally as a training mechanism, but the discriminator is not GAN's end product or purpose; the generator is. Search algorithm optimization (D) is an information-retrieval concern entirely outside generative modeling's scope.
GANs are one of several generative model families tested in this domain alongside diffusion models (which power the U-Net-based denoising questions elsewhere in this set) and variational autoencoders, each with different training dynamics and stability characteristics.
Reference: Core Machine Learning and AI Knowledge domain - GANs, generative modeling.
NEW QUESTION # 20
You're building a multimodal model to predict stock prices using news articles (text) and historical price data (time series). You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques would be MOST effective in addressing this issue?
Answer: D
Explanation:
All the listed techniques combat overfitting. Increasing data size provides more examples. Regularization penalizes complex models. Early stopping prevents training too long on the training data. Data augmentation creates more robust and generalizable model by artifically expanding data through techniques like Tme series perturbation and Text paraphrasing.
NEW QUESTION # 21
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