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NVIDIA Generative AI Multimodal Sample Questions (Q169-Q174):

NEW QUESTION # 169
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. A model that converts all data into a single text format and uses a large language model (LLM) for prediction.
C. Separate models for each modality trained independently, and then ensembled together at the prediction stage.
D. 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.
E. A simple feed forward neural network with concatenated features from all modalities.

Answer: A,D

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 # 170
You are working on a multimodal sentiment analysis task where you have both textual reviews and corresponding product images. You want to build an attention mechanism to identify the most relevant parts of the image that contribute to the sentiment expressed in the text. Which of the following attention mechanisms is BEST suited for generating spatial attention maps highlighting these relevant regions in the image?

A. Spatial attention in the image encoder, conditioned on the text embedding (e.g., attention over image features based on text query).
B. Self-attention in the text encoder (e.g., Transformer).
C. Global average pooling of image features.
D. Temporal attention in a video encoder.
E. Channel attention in the image encoder (e.g., Squeeze-and-Excitation).

Answer: A

Explanation:
Spatial attention, conditioned on the text embedding, directly addresses the task. This mechanism allows the model to focus on specific regions of the image that are most relevant to the sentiment expressed in the text. The text embedding acts as a 'query' to attend over the image features, generating a spatial attention map that highlights the contributing regions. Self attention in text (A) focuses on relationships within the text itself. Channel attention (B) focuses on feature channel importance, not spatial localization related to the text. Temporal attention (D) is irrelevant for static images. Global average pooling (E) loses spatial information.

NEW QUESTION # 171
Consider a scenario where you are training a multimodal Generative A1 model using both image and text dat a. The image data is stored in a directory with millions of high-resolution images, and the text data is in a large CSV file. What is the MOST efficient way to load and preprocess this data for training, minimizing memory usage and maximizing throughput?

A. Use a distributed file system to store the data and load it directly into the model during training.
B. Load all images and text data into memory at once, preprocess them, and then feed them to the model.
C. Convert all images to a single large video file and load it into memory along with the text data.
D. Use a data generator (e.g., TensorFlow's 'tf.data.Dataset' or PyTorch's *DataLoadeN) to load and preprocess data in batches.
E. Resize all images to a very small size before loading them into memory.

Answer: D

Explanation:
Data generators are the most efficient way to handle large datasets because they load and preprocess data in batches, minimizing memory usage. Option A is infeasible for large datasets. Option C is not a standard or efficient approach. Option D is relevant for distributed training but doesn't address the memory issue of loading the entire dataset. Option E reduces memory usage but may sacrifice important image details.

NEW QUESTION # 172
You are evaluating two different generative A1 model architectures (Model A and Model B) for image generation. You use the Frechet Inception Distance (FID) score as your primary evaluation metric. Model A has a lower FID score than Model B. Which of the following statements are MOST accurate regarding the interpretation of the FID scores? (Select TWO)

A. Model A generates images that are more visually appealing to human observers.
B. Model A generates images that have a distribution more similar to the real image distribution used for calculating the FID score.
C. Model B necessarily has better performance on downstream tasks using the generated images.
D. Model B generates images that are more diverse than Model A.
E. Model A is less likely to suffer from mode collapse than Model B.

Answer: B,E

Explanation:
A lower FID score indicates that the generated images are statistically more similar to the real images (B). It also suggests that Model A is less prone to mode collapse (D), as it captures the data distribution better. FID score doesn't guarantee visual appeal (A) or better performance on downstream tasks (E). Diversity (C) isn't directly implied by a lower FID score alone.

NEW QUESTION # 173
When deploying a Generative A1 model to a resource-constrained edge device (e.g., a mobile phone), what are the key considerations for model optimization and which techniques are most effective?

A. Increasing the model's complexity to improve accuracy on the limited hardware.
B. A and B.
C. Focusing solely on reducing the model's parameter count without considering its computational complexity.
D. Model quantization (e.g., INT8) to reduce model size and memory bandwidth, and model pruning to remove unimportant connections.
E. Knowledge distillation to transfer knowledge from a large, accurate model to a smaller, faster model suitable for the edge.

Answer: B

Explanation:
Edge deployment requires optimizing for both model size and computational efficiency. Quantization reduces model size, pruning removes unnecessary connections, and knowledge distillation creates smaller, faster models. Increasing model complexity is counterproductive in resource-constrained environments. Both parameter count and computational complexity are important factors.

NEW QUESTION # 174
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