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NVIDIA Generative AI Multimodal Sample Questions (Q323-Q328):

NEW QUESTION # 323
You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?

A. Replace the image processing component with a simpler, less powerful model.
B. Reduce the weight of the image modality in the overall loss function.
C. Implement a modality dropout strategy during training, randomly masking either the text or image input to force the model to rely more on the available modality.
D. Increase the size of the training dataset by only adding more data with high quality images.
E. Exclusively train the model on articles with high-quality images to improve its visual processing capabilities.

Answer: C

Explanation:
Modality dropout forces the model to learn robust representations from each modality independently, making it less reliant on the presence of both modalities. This improves performance when one modality is missing or of low quality. Training only on high-quality images (A) would exacerbate the problem. Reducing the image modality's weight (C) might help slightly but doesn't fundamentally address the issue. Using a simpler image model (D) would likely decrease overall performance. Increasing the training dataset size with only high-quality images (E) will not address the problem of the model's dependence on high-quality images.

NEW QUESTION # 324
You are building a multimodal model to predict stock prices using financial news articles (text), historical stock prices (time-series), and company logos (images). You have preprocessed the data and are ready to train your model. Which of the following architectures would be MOST suitable for effectively integrating these three modalities?

A. A model that uses a Transformer encoder for each modality, followed by a shared Transformer decoder for prediction, enabling cross-modal attention at the decoder level.
B. Separate models for each modality trained independently, and then ensembled together at the prediction stage.
C. A model that combines a Transformer for text, an LSTM for time-series, and a CNN for images, with a late fusion strategy using a weighted averaging of predictions.
D. A simple feed forward neural network with concatenated features from all modalities.
E. A model that converts all data into a single text format and uses a large language model (LLM) for prediction.

Answer: A,C

Explanation:
Combining a Transformer for text, an LSTM for time-series, and a CNN for images with a late fusion approach allows each modality to be processed by a suitable architecture and then combined to generate a final prediction. Using transformers in each modality with shared Transformer decoder can efficiently integrate and predict stock prices using cross modal attention . A simple feedforward network is unlikely to capture the temporal dependencies in the time-series data or the complex relationships between modalities. Ensembling independent models doesn't allow for cross-modal learning. Converting all data into text might lose valuable information from the other modalities. Therefore, hybrid architecture combining transformers, LSTMs, and CNNs with cross-modal attention or late fusion would be most effective.

NEW QUESTION # 325
You're working with a Text-to-Image generation model and want to improve its explainability by visualizing which words in the text prompt are most influential in generating specific regions of the output image. What technique would be most suitable for achieving this?

A. Visualizing the latent space embeddings of the generated images.
B. Attention maps from the cross-attention layers between the text encoder and the image decoder.
C. Gradient-weighted Class Activation Mapping (Grad-CAM) on the image encoder.
D. Analyzing the frequency of words in the generated image captions.
E. SHAP values applied to the text encoder.

Answer: B

Explanation:
Attention maps from the cross-attention layers directly indicate which words in the text prompt the image decoder attends to when generating different regions of the image. This provides a visual explanation of the relationship between text and image generation. Grad-CAM is more suited for explaining image classification models. SHAP values can explain the importance of words in the text, but not their spatial influence in the generated image.

NEW QUESTION # 326
You are building a multimodal RAG application that integrates text documents and images. You've noticed that when a user query relates strongly to the visual content, the retrieved documents are less relevant than desired. Which of the following strategies would MOST effectively improve the retrieval of relevant information in this scenario?

A. Use a larger language model for the generative component of the RAG pipeline.
B. Increase the k value (number of retrieved documents) in the vector search.
C. Prepend the image captions to all the source documents to enhance text-based retrieval.
D. Fine-tune the existing text embedding model with more text data.
E. Implement cross-modal embedding, training a model to create a joint embedding space for text and images.

Answer: E

Explanation:
Cross-modal embedding allows you to represent both text and images in a shared vector space. This enables the retrieval system to understand the relationship between text and visual content, leading to more relevant document retrieval when the query relates to images. Increasing 'k' might retrieve more irrelevant documents. A larger LLM and fine-tuning the text embedding model won't directly address the core issue of multimodal understanding.

NEW QUESTION # 327
You are developing a multimodal generative A1 model that takes both image and text inputs. The image branch uses a ResNet50 pre- trained on ImageNet, while the text branch uses a BERT model. To effectively combine the features, you need to align their representations. Which of the following techniques is MOST suitable for projecting the image and text features into a common embedding space?

A. Direct concatenation of ResNet50 and BERT output features.
B. Employing Contrastive Learning with a shared embedding space and using positive and negative pairs of image and text.
C. Fine-tuning the entire ResNet50 and BERT models jointly on the multimodal dataset.
D. Training separate linear projection layers for both ResNet50 and BERT outputs, followed by concatenation.
E. Using Principal Component Analysis (PCA) to reduce the dimensionality of ResNet50 and BERT features before concatenation.

Answer: B

Explanation:
Contrastive learning is highly effective for aligning representations from different modalities. By training the model to pull together embeddings of related image-text pairs while pushing apart embeddings of unrelated pairs, it learns a shared embedding space where semantically similar concepts are close to each other, regardless of their modality. While (B) is a possible approach, it doesn't explicitly enforce alignment based on semantic similarity. (A) is unlikely to produce good results due to differing feature spaces. (C) is computationally expensive. (D) is a dimensionality reduction technique, not primarily an alignment method.

NEW QUESTION # 328
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New NCA-GENM Test Experience & Upgrade NCA-GENM Dumps

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NVIDIA Generative AI Multimodal Sample Questions (Q323-Q328):

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