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| Section | Objectives |
|---|---|
| Topic 1: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Topic 2: Generative AI Concepts | - Generative models
|
| Topic 3: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 4: Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Topic 5: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
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NEW QUESTION # 29
You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?
Answer: B
Explanation:
Classifier-free guidance allows you to control the influence of the text description on the generated image. By adjusting the guidance scale, you can find a balance between generating images that are faithful to the text description and generating diverse and high-quality images. Increasing diffusion steps, batch size, or reducing the learning rate may help but is less targeted towards improving fidelity specifically.
NEW QUESTION # 30
You are training a text-to-image diffusion model and observe that the generated images often exhibit a 'washed-out' or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?
Answer: B
Explanation:
A perceptual loss function encourages the generated images to have more realistic features and details, as it compares the high- level representations of the generated images to the real images. Increasing its weight in the training objective would incentivize the model to produce more detailed and visually appealing results. Decreasing diffusion steps leads to faster but often lower-quality results. Reducing batch size can affect training stability but doesn't directly address the 'washed-out' appearance. Data augmentation and learning rate adjustments may have some impact, but are less directly targeted at improving image detail.
NEW QUESTION # 31
You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?
Answer: B
Explanation:
CycleGAN is designed for unpaired image-to-image translation. In this scenario, it can be adapted to translate between the image and text modalities without requiring paired data. One generator learns to generate images from text, while another learns to generate text from images. Cycle consistency ensures that translating an image to text and then back to an image results in an image similar to the original. Vanilla GANI cGAN, and DCGAN are not inherently designed for bi- directional translation between modalities without paired data. SRGAN is for image super-resolution.
NEW QUESTION # 32
Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?
Answer: A
Explanation:
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.
NEW QUESTION # 33
You are building a retrieval-augmented generation (RAG) system that utilizes a knowledge graph to enhance the responses generated by a large language model. The knowledge graph contains information about entities and their relationships extracted from both text documents and image metadat a. However, you observe that the system often retrieves irrelevant or outdated information from the knowledge graph, leading to inaccurate or misleading responses. Which of the following strategies would be MOST effective in addressing this issue?
Answer: C
Explanation:
Filtering and ranking the retrieved information based on relevance and recency ensures that the system prioritizes the most accurate and up-to-date information from the knowledge graph. Simply increasing the size of the knowledge graph or using a simpler language model would not directly address the issue of irrelevant or outdated information.
NEW QUESTION # 34
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