NVIDIA NCA-GENM EXAM DUMPS IN PDF FORMAT

NVIDIA NCA-GENM Exam Dumps in PDF Format

NVIDIA NCA-GENM Exam Dumps in PDF Format

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Tags: Test NCA-GENM Questions Pdf, NCA-GENM Test Study Guide, Sample NCA-GENM Questions Answers, NCA-GENM Latest Test Prep, NCA-GENM Premium Exam

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NVIDIA Generative AI Multimodal Sample Questions (Q317-Q322):

NEW QUESTION # 317
You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?

  • A. Using word embeddings (e.g., Word2Vec, GloVe) for text, pre-trained CNN features (e.g., from ResNet, Inception) for images, and embedding user profiles using a graph embedding technique.
  • B. Using a combination of TF-IDF for text, pixel values for images, and numerical features for user profile information. Then apply PCA for dimensionality reduction.
  • C. Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.
  • D. Using bag-of-words for text, histogram of oriented gradients (HOG) for images, and simple numerical features (e.g., number of followers) for user profiles.
  • E. Using TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.

Answer: A

Explanation:
Using word embeddings captures semantic meaning in text. Pre-trained CNN features provide high-level image representations. Graph embedding for user profiles captures relationships between users. These advanced techniques provide better representations than simple methods like TF-IDF, pixel values, or bag-of-words.


NEW QUESTION # 318
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. Mean Squared Error (MSE) loss between the generated image and a downscaled version of the target image.
  • C. Increasing the batch size during training to improve gradient estimation.
  • 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.

Answer: D

Explanation:
Perceptual loss is specifically designed to capture high-level perceptual similarities between images, leading to sharper and more realistic outputs- MSE and L1 losses tend to produce blurry images as they penalize pixel-wise differences without considering perceptual similarity. Cross-entropy loss is relevant for classification tasks, not image generation directly from text descriptions. Increasing the batch size can improve training stability but does not directly address the lack of fine-grained details.


NEW QUESTION # 319
You are tasked with evaluating a multimodal A1 model that combines image and text inputs to generate product descriptions. You observe that the model performs well on common product categories (e.g., clothing, electronics) but struggles with niche categories (e.g., antique furniture, scientific instruments). Which of the following strategies would be MOST effective in improving the model's performance on niche categories?

  • A. Implement data augmentation techniques to create synthetic data for niche categories.
  • B. Fine-tune the model on a dataset specifically curated for niche product categories.
  • C. Replace the image encoder with a more powerful architecture.
  • D. Increase the overall size of the training dataset.
  • E. Decrease the learning rate during training.

Answer: B

Explanation:
Fine-tuning on a niche dataset addresses the specific lack of knowledge about those categories. While other options might offer marginal improvements, targeted fine-tuning is the most direct and effective approach. Data augmentation (E) could help, but is secondary to using real-world data for fine-tuning.


NEW QUESTION # 320
You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?

  • A. Perplexity
  • B. ROUGE score
  • C. Inception Score
  • D. CIDEr score
  • E. BLEU score

Answer: D

Explanation:
CIDEr (Consensus-based Image Description Evaluation) is specifically designed for image captioning and is highly correlated with human judgments of caption quality. While BLEU and ROUGE are useful for general text generation, CIDEr excels at capturing semantic similarity in image captions. Inception Score assesses the quality of generated images, not captions, and Perplexity measures the uncertainty of a language model.


NEW QUESTION # 321
You are tasked with building a Generative A1 model that can generate realistic images of birds based on text descriptions. You have a large dataset of bird images and corresponding text captions. Which of the following architectures is MOST suitable for this task, considering both image quality and training efficiency?

  • A. An Image Transformer model trained from scratch.
  • B. A Variational Autoencoder (VAE) trained on the image dataset.
  • C. A standard Convolutional Neural Network (CNN) for image generation.
  • D. A simple Recurrent Neural Network (RNN) to generate pixel values sequentially.
  • E. A Generative Adversarial Network (GAN) conditioned on the text descriptions (e.g., a StackGAN or AttnGAN).

Answer: E

Explanation:
GANs, especially those conditioned on text descriptions, are specifically designed for generating realistic images based on textual input. StackGAN and AttnGAN are examples that have shown good performance. CNNs and VAEs are less effective for high-quality image generation from text. RNNs are not well-suited for generating images pixel by pixel. Image transformers are possible, but computationally expensive for training from scratch on a large dataset like this, although diffusion models based on transformers are becoming more popular


NEW QUESTION # 322
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