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The NVIDIA Generative AI Multimodal (NCA-GENM) study material of CramPDF is available in three different and easy-to-access formats. The first one is printable and portable NVIDIA Generative AI Multimodal (NCA-GENM) PDF format. With the PDF version, you can access the collection of actual NVIDIA Generative AI Multimodal (NCA-GENM) questions with your smart devices like smartphones, tablets, and laptops.
| Section | Objectives |
|---|---|
| Topic 1: Generative AI Concepts | - Generative models
|
| Topic 2: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Topic 3: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 4: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Topic 5: Multimodal AI Systems | - Cross-modal learning
|
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NEW QUESTION # 15
What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?
Answer: C
Explanation:
In a denoising diffusion probabilistic model (DDPM), the U-Net serves as the noise-prediction network at the core of the iterative generation process: at each reverse-diffusion timestep, the U-Net takes the current noisy image (and typically a timestep embedding, plus conditioning information like a CLIP text embedding in text- to-image models) as input and predicts the noise component present at that step. Subtracting this predicted noise incrementally, over many timesteps starting from pure Gaussian noise, progressively denoises the input into a coherent image - the mechanism by which DDPMs generate new images from pure noise. U-Net's architecture - a contracting encoder path paired with an expanding decoder path, connected by skip connections at matching resolutions - is well suited to this role because the skip connections preserve fine- grained spatial detail that would otherwise be lost through the network's downsampling bottleneck, which matters for producing sharp, high-fidelity denoised output at each step.
Options B, C, and D describe discriminative tasks - classification, detection, and segmentation - that describe *other* legitimate applications of U-Net-style architectures (originally developed for biomedical image segmentation) but do not describe its function specifically *within* the diffusion generative process.
Within a DDPM pipeline specifically, U-Net's role is generative noise prediction supporting image synthesis from noise, not classification or detection of any kind.
Reference: Core Machine Learning and AI Knowledge domain - diffusion models, U-Net noise-prediction architecture.
NEW QUESTION # 16
You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?
Answer: E
Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.
NEW QUESTION # 17
You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?
Answer: A,B,D
Explanation:
Addressing skewness is crucial for preventing the model from being biased towards dominant modalities. Oversampling, modality- specific weighting, and a biased-aware loss function are all effective strategies for mitigating this problem.
NEW QUESTION # 18
You're tasked with building a model that can generate recipes from images of food. You decide to use a Variational Autoencoder (VAE) architecture. What would be a suitable loss function combination for this task, considering both reconstruction accuracy and recipe relevance?
Answer: B
Explanation:
The Reconstruction loss ensures the generated image is similar to the input. KL divergence enforces a smooth latent space. The Cross-entropy loss ensures the generated recipe is relevant to the decoded image. Perceptual loss, while helpful for image quality, doesn't directly address recipe relevance. Using a text embedding of a random recipe would not guide the model towards generating relevant recipes.
NEW QUESTION # 19
Consider the following code snippet used in training a multimodal model:
During experimentation, you discover that the image modality contributes negligibly to the final prediction. How would you modify the training loop to dynamically adjust the importance of each modality?
Answer: E
Explanation:
Dynamically scaling gradients based on their magnitude allows the model to automatically adjust the importance of each modality during training. If the image gradients are small compared to the text gradients, the scaling factor will increase their influence, encouraging the model to learn from the image modality. Modality dropout is helpful, however gradient scaling provides finer control.
NEW QUESTION # 20
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