NVIDIA Generative AI Multimodal - NCA-GENM FREE EXAM DUMPS QUESTIONS & ANSWERS]

Question 1

You are working on a sequence-to-sequence model for neural machine translation. You've implemented an attention mechanism, but the model is still struggling with long sentences, often losing context in the later parts of the translation. Which type of attention mechanism is most likely to alleviate this issue effectively?

A. Self-Attention B. Global (Soft) Attention C. Bahdanau Attention (Additive Attention) D. Multi-Head Attention E. Local (Hard) Attention

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Correct Answer: D Vote an answer

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Question 2

You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)

A. Increasing the size of the vocabulary. B. Adding L1 regularization to the embedding layer. C. Applying label smoothing to the target sequences. D. Adding dropout to the encoder and decoder layers. E. Decreasing the number of attention heads.

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Correct Answer: C,D Vote an answer

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Question 3

Which of the following techniques is most appropriate for mitigating the vanishing gradient problem in very deep neural networks, particularly when training generative models?

A. Data augmentation B. Early stopping C. Dropout D. Residual connections (skip connections) E. Weight decay

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Correct Answer: D Vote an answer

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Question 4

You observe that the generated images often lack fine-grained details and tend to be blurry. Which of the following techniques could MOST effectively improve the visual quality of the generated images?

A. Increasing the batch size during training. B. Using a larger dataset of text-image pairs. C. Using a variational autoencoder (VAE) instead of a GAN.unlikely to significantly improve diagnosis accuracy. D. Decreasing the learning rate during training. E. Implementing a discriminator network and using adversarial training (GAN).

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Correct Answer: E Vote an answer

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Question 5

Consider a scenario where you're training a generative A1 model to create realistic images from text descriptions. You notice that the generated images lack fine-grained details and appear blurry. Which of the following loss functions or training techniques could you employ to improve the image quality and sharpness?

A. L1 loss between the generated image and the target image. B. Increasing the batch size during training to improve gradient estimation. C. Mean Squared Error (MSE) loss between the generated image and a downscaled version of the target image. D. Perceptual loss, which compares the feature representations of the generated and target images in a pre-trained CNN. E. Cross-entropy loss between the generated image and the text description.

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Correct Answer: D Vote an answer

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Question 6

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?

A. Use a curriculum learning approach where the model is initially trained only on the text modality, and the image modality is gradually introduced. B. Implement a separate loss function for the image modality and adjust its weight based on validation performance. C. Compute modality-specific gradients and apply a scaling factor to the image gradients based on their magnitude relative to the text gradients. D. Apply a fixed weight to the image features before feeding them into the model. E. Introduce a modality dropout mechanism that randomly drops either the image or text modality during each training iteration.

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Correct Answer: C Vote an answer

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Question 7

Consider a multimodal A1 system that generates recipes based on images of ingredients. The system uses attention maps to highlight the relevant ingredients in the image. You observe that the attention maps are often noisy and highlight irrelevant parts of the image, leading to incorrect recipes. Which of the following strategies could BEST improve the quality and interpretability of the attention maps?

A. Apply L1 regularization to the attention weights to encourage sparsity. B. Use a stronger image encoder, such as a larger ResNet or a Vision Transformer. C. All of the above can improve the quality and interpretability of the attention maps. D. Add more layers to the attention module. E. Increase the size of the convolutional filters in the image encoder.

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Correct Answer: A,B Vote an answer

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Question 8

Which of the following statements accurately describes the purpose and functionality of 'LoRA' (Low-Rank Adaptation) in the context of fine-tuning large language models?

A. LoRA is a type of attention mechanism used in transformer models. B. LoRA is a method for compressing the weights of a pre-trained language model to reduce its memory footprint. C. LoRA is a regularization technique used to prevent overfitting during fine-tuning. D. LoRA is a data augmentation technique used to increase the size of the training dataset. E. LoRA is a fine-tuning technique that freezes the original weights of a pre-trained model and trains a small set of low-rank matrices to adapt the model to a specific task.

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Correct Answer: E Vote an answer

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Question 9

You are building a real-time multimodal application that requires processing both audio and video streams simultaneously. You need to minimize the latency of the system while maximizing throughput. Which of the following hardware and software optimizations would be most effective?

A. Using a high-latency, high-bandwidth network connection. B. Using a CPU-based implementation for both audio and video processing. C. Using separate GPUs for audio and video processing and employing asynchronous data transfer techniques. D. Offloading both audio and video processing to a single high-end GPIJ. E. Compressing the audio and video streams aggressively to reduce the amount of data that needs to be processed.

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Correct Answer: C Vote an answer

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Question 10

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. 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. B. Using only bilinear interpolation in the upsampling layers to avoid introducing artifacts. NeMo can assist by providing pre-trained interpolation layers. C. Using only transpose convolutional layers for upsampling to learn the optimal upsampling filters. NeMo offers optimized transpose convolution implementations for performance. D. Ignoring the low resolution features and concentrate on better latent space sampling. NeMo can provide models to enhance sampling techniques. E. 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.

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Correct Answer: A Vote an answer

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Question 11

You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?

A. Switching to a larger model architecture with more parameters. B. Using a simpler loss function during training. C. Increasing the batch size during inference to better utilize GPU resources. D. Removing dropout layers from the model. E. Implementing TensorRT for model optimization and quantization.

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Correct Answer: E Vote an answer

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Question 12

You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?

A. Training separate LSTMs for visual and audio features and averaging their outputs. B. Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM. C. Using a simple feedforward network to combine the ResNet and Wav2Vec features. D. Ignoring the audio track and only using the video frames. E. Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.

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Question 13

Which NVIDIA SDK would be most appropriate for building a real-time, interactive avatar that can respond to voice commands and generate realistic facial expressions?

A. Avatar Cloud Engine (ACE) B. Triton Inference Server C. NeMo D. RAPIDS E. Riva

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Correct Answer: A Vote an answer

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Question 14

You are building a real-time multimodal system that processes live video and audio streams to detect potentially dangerous situations. Latency is a critical constraint. Which of the following strategies is MOST important to minimize latency in this system?

A. Using a very deep neural network to achieve the highest possible accuracy, regardless of latency. B. Optimizing the model architecture for efficient computation, using techniques like model quantization, knowledge distillation, and reducing the number of layers. C. Employing extensive data augmentation during training. D. Using a large batch size during inference to maximize GPU utilization. E. Converting the video stream to text transcripts before processing.

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Correct Answer: B Vote an answer

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