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NVIDIA Generative AI Multimodal Sample Questions (Q249-Q254):

NEW QUESTION # 249
You're working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?

A. Training the model on a larger dataset of music.
B. Increasing the size of the model's hidden layers.
C. Adding more layers to the model.
D. Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.
E. Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.

Answer: D,E

Explanation:
A VAE can learn a structured latent space that captures essential musical features, allowing for controlled generation. RNNs with attention are well-suited for modeling sequential data like music, capturing long-range dependencies and creating a more coherent structure. Simply increasing the size or depth of the model may not address the underlying issue of musical structure. A larger dataset may help, but structured modeling techniques are generally more effective.

NEW QUESTION # 250
You're fine-tuning a pre-trained multimodal model for a specific downstream task. You notice that while the model's performance on the training data is excellent, it performs poorly on unseen dat a. What regularization technique, beyond standard weight decay, is MOST likely to improve the model's generalization ability in this scenario, and what is its purpose?

A. Layer Normalization: To normalize activations across features, stabilizing training.
B. Early Stopping: To halt training when performance on a validation set degrades.
C. Dropout: To randomly deactivate neurons during training, preventing co-adaptation and improving robustness.
D. Batch Normalization: To accelerate training and reduce internal covariate shift.
E. Gradient Clipping: To prevent exploding gradients, stabilizing training.

Answer: C

Explanation:
Dropout is particularly effective at preventing co-adaptation of neurons, forcing the model to learn more robust and independent features. This directly combats overfitting, leading to improved generalization. While Batch and Layer Normalization help with training stability, they don't directly address overfitting as effectively as Dropout. Early Stopping is a good practice, but doesn't actively regularize during training. Gradient clipping addresses training stability, not generalization.

NEW QUESTION # 251
You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?

A. Increasing the embedding dimension-
B. Using a larger batch size during training.
C. Implementing hard negative mining.
D. Using a simpler model architecture-
E. Decreasing the temperature parameter in the contrastive loss.

Answer: C

Explanation:
Hard negative mining focuses on selecting the most challenging negative examples (incorrect image-text pairs) during training. This forces the model to learn more robust and discriminative embeddings that generalize better to unseen combinations. Increasing embedding dimension or using larger batch size might help to some extent, but hard negative mining directly addresses the core issue of distinguishing similar but incorrect pairs. Decreasing the temperature parameter can make the contrastive loss too sensitive, potentially hindering generalization. A simpler model architecture may be detrimental if it lacks the capacity to capture the complex relationships

NEW QUESTION # 252
You're developing a system that analyzes video footage and generates textual summaries of the events occurring in the video. Which of the following architectures would be the MOST appropriate starting point for this task?

A. A purely convolutional neural network (CNN) for image classification.
B. A long short-term memory (LSTM) network for time series forecasting.
C. A support vector machine (SVM) for classification.
D. A generative adversarial network (GAN) for image generation.
E. A transformer-based encoder-decoder architecture, with the encoder processing video frames (e.g., using a 3D CNN) and the decoder generating text.

Answer: E

Explanation:
Transformer-based encoder-decoder architectures are well-suited for sequence-to-sequence tasks like video captioning. The encoder can process the video frames to extract features, and the decoder can generate the corresponding textual summary. A 3D CNN can be used to extract spatio-temporal features from the video.

NEW QUESTION # 253
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

A. Using a simple Multi-Layer Perceptron (MLP) as the generator.
B. Implementing spectral normalization in both the generator and discriminator.
C. Using a deeper and wider generator network (e.g., with more layers and channels).
D. Using a more powerful discriminator architecture (e.g., with attention mechanisms).
E. Reducing the size of the input noise vector to the generator.

Answer: B,C

Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between real and fake images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.

NEW QUESTION # 254
......

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