LATEST UPDATED NCA-GENM VALID TEST SIMS | AMAZING PASS RATE FOR NCA-GENM EXAM | TOP NCA-GENM: NVIDIA GENERATIVE AI MULTIMODAL

Latest updated NCA-GENM Valid Test Sims | Amazing Pass Rate For NCA-GENM Exam | Top NCA-GENM: NVIDIA Generative AI Multimodal

Latest updated NCA-GENM Valid Test Sims | Amazing Pass Rate For NCA-GENM Exam | Top NCA-GENM: NVIDIA Generative AI Multimodal

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Tags: NCA-GENM Valid Test Sims, NCA-GENM New Braindumps Pdf, NCA-GENM Latest Test Format, Lab NCA-GENM Questions, NCA-GENM Study Demo

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NVIDIA Generative AI Multimodal Sample Questions (Q97-Q102):

NEW QUESTION # 97
Consider a scenario where you are building a system for emotion recognition using facial expressions (images) and spoken words (audio). You plan to use a Convolutional Neural Network (CNN) for image feature extraction and a Recurrent Neural Network (RNN) for audio feature extraction. You want to combine the features learned by these networks using a cross-modal attention mechanism. Which of the following statements BEST describes how cross-modal attention can improve the performance of your system?

  • A. Cross-modal attention reduces the computational complexity of the model by simplifying the feature extraction process.
  • B. Cross-modal attention allows the model to focus on the most relevant parts of one modality based on the information from the other modality.
  • C. Cross-modal attention is only effective when the data from both modalities is perfectly synchronized.
  • D. Cross-modal attention ensures that the data from both modalities is perfectly aligned in time.
  • E. Cross-modal attention forces the CNN and RNN to learn identical feature representations.

Answer: B

Explanation:
Cross-modal attention enables the model to selectively focus on the most relevant information from one modality by attending to the cues provided by the other modality. This allows the model to learn more nuanced relationships between the modalities and improve overall performance. It doesn't force identical feature representations or reduce computational complexity. While synchronization can help, attention mechanisms can still function even without perfect temporal alignment.


NEW QUESTION # 98
You are tasked with building a system that can answer questions based on both an image and a corresponding text description. The image is represented as a feature vector from a CNN, and the text is represented as a sequence of word embeddings from a pre-trained language model. Which architecture would be most suitable for this task?

  • A. A simple feedforward neural network that concatenates the image and text feature vectors.
  • B. A Transformer-based architecture with cross-attention mechanisms that allow the model to attend to both the image and text features simultaneously.
  • C. Two separate models, one for processing images and another for processing text, with the final answer being chosen based on the higher confidence score.
  • D. A recurrent neural network (RNN) that processes the text and then uses the final hidden state to attend to the image features.
  • E. A combination of a CNN and an LSTM, where the CNN processes the image and the LSTM processes the text independently.

Answer: B

Explanation:
A Transformer-based architecture with cross-attention is best suited for this task. It allows the model to learn complex relationships between the image and text features, enabling it to answer questions that require understanding of both modalities. RNNs can be effective but might struggle with long-range dependencies. Simple feed forward networks lack the ability to capture sequential information in the text. Two seperate models cannot understand both the image and text features simultaneously. CNN and LSTM are not adequate for this type of question answering task.


NEW QUESTION # 99
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?

  • A. Implement a mechanism to filter and rank the retrieved information based on relevance and recency, using both semantic similarity and temporal information.
  • B. Increase the size of the knowledge graph.
  • C. None of the above.
  • D. Reduce the number of entities in the knowledge graph.
  • E. Use a simpler language model for the generative component of the RAG pipeline.

Answer: A

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 # 100
Which of the following techniques is LEAST likely to improve the performance of a Generative A1 model tasked with generating realistic images from text descriptions?

  • A. Using a more powerful generative architecture, such as a Transformer-based diffusion model.
  • B. Reducing the dimensionality of the text embeddings used as input to the image generator.
  • C. Increasing the size of the training dataset with high-quality image-text pairs.
  • D. Applying data augmentation techniques to the training images, such as random cropping and rotations.
  • E. Implementing classifier-free guidance during the diffusion process.

Answer: B

Explanation:
Reducing the dimensionality of text embeddings will likely degrade performance, as it removes information that the model needs to accurately generate images. The other options (A, B, C, and E) are all established techniques for improving the quality and fidelity of generated images.


NEW QUESTION # 101
Consider the following Python code snippet used for processing image and text data for a multimodal model:

What is the primary limitation of the text encoding method used in this code, and how could it be improved for use in a real-world multimodal model?

  • A. The text encoding is overly complex and should be simplified to reduce computational overhead.
  • B. It adequately addresses the complexities inherent in natural language, making it suitable for a variety of multimodal models.
  • C. The text encoding is suitable for small datasets but will not scale to larger datasets.
  • D. The text encoding is efficient but incompatible with common deep learning architectures.
  • E. The text encoding only supports ASCII characters and does not account for word embeddings or sequence length variations. Use a tokenizer like BERT or SentencePiece to generate embeddings and pad sequences to a fixed length

Answer: E

Explanation:
The code simply converts each character to its ASCII value, which loses semantic meaning and doesn't handle variable-length sequences or word relationships. Real-world models use tokenizers and embeddings to capture the nuances of language. BERT and SentencePiece are common tools for this. Option B accurately describes this limitation and suggests a practical solution.


NEW QUESTION # 102
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