NVIDIA Generative AI Multimodal : NCA-GENM

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最新的 NVIDIA-Certified Associate NCA-GENM 免費考試真題:

1. You are tasked with building a multimodal generative A1 model that takes both image and text as input to generate a coherent video. Which of the following architectures is MOST suitable for this task, considering the need to fuse information from different modalities and generate sequential data?

A) A Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.
B) A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.
C) A standard Convolutional Neural Network (CNN) followed by a fully connected layer.
D) A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.
E) A simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.

2. You're designing a U-Net architecture for generating high-resolution medical images from low-resolution scans. Which of the following considerations are MOST crucial for maintaining fine-grained detail during the upsampling process, and how might NVIDIA's NeMo framework assist?

A) Using only transpose convolutional layers for upsampling to learn the optimal upsampling filters. NeMo offers optimized transpose convolution implementations for performance.
B) Incorporating skip connections from the contracting path to the expanding path, allowing the network to leverage high-resolution features from earlier layers. NeMo provides modules for efficient skip connection implementation and management of feature map sizes.
C) Ignoring the low resolution features and concentrate on better latent space sampling. NeMo can provide models to enhance sampling techniques.
D) Employing a very deep network architecture to capture complex relationships between pixels. NeMo aids in managing the complexity and training of such deep networks with optimized optimizers and distributed training capabilities.
E) Using only bilinear interpolation in the upsampling layers to avoid introducing artifacts. NeMo can assist by providing pre-trained interpolation layers.

3. You are training a multimodal model that combines audio and video dat
a. You observe that the model performs well on the training data but generalizes poorly to unseen data. Which of the following regularization techniques is MOST likely to improve the generalization performance in this scenario?

A) Early Stopping
B) Data Augmentation
C) L1 Regularization (Lasso)
D) Weight Decay (L2 Regularization)
E) Dropout

4. You are developing a system that generates 3D models from text descriptions. The system currently produces models that are geometrically accurate but lack fine-grained surface details and realistic textures. Which of the following steps would be MOST effective in improving the visual realism of the generated 3D models?

A) Use a simpler text encoder to focus on geometric information.
B) Increase the number of polygons used to represent the 3D models.
C) Rely solely on procedural generation techniques.
D) Reduce the size of the training dataset.
E) Train a separate texture generation model conditioned on the text description and the generated 3D geometry.

5. You are building a generative A1 model that combines text and image inputs to generate novel images. You have access to NVIDIA NeMo and want to leverage its pre-trained models and tools. Which NeMo modules or features would be MOST beneficial for this multimodal task? (Select all that apply)

A) NeMo's core building blocks for constructing custom neural network architectures.
B) NeMo's pre-trained language models for text understanding and feature extraction.
C) NeMo's TTS models for generating image descriptions.
D) NeMo's ASR models for processing text inputs.
E) NeMo's support for PyTorch Lightning for efficient training and scaling.

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