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NVIDIA Generative AI Multimodal Sample Questions:

1. You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)

A) Increase the learning rate for the audio encoder.
B) Use a contrastive loss function that encourages alignment between text and audio representations.
C) Reduce the size of the text encoder.
D) Replace audio features with raw audio waveform.
E) Apply modality-specific dropout to the text encoder.

2. You are training a variational autoencoder (VAE) for generating images. The generated images are blurry and lack fine details. Which of the following modifications to the VAE architecture or training procedure would be MOST effective in improving the image quality?

A) Using a deeper latent space (higher dimensionality).
B) Using a smaller batch size.
C) Increasing the capacity of the encoder and decoder networks.
D) Decreasing the value of the KL divergence weight in the loss function.
E) Using a larger batch size.

3. You are working on a project to classify images of different types of flowers. You have a relatively small dataset (around 500 images per class). Which of the following techniques would be the MOST effective to improve the performance of your image classifier, considering the limited data?

A) Train a very deep convolutional neural network from scratch-
B) Use a simple linear classifier.
C) Reduce the image resolution to decrease the number of parameters in the model.
D) Apply aggressive data augmentation techniques, such as random rotations, flips, and crops.
E) Use a pre-trained convolutional neural network on a large dataset like ImageNet and fine-tune it on your flower dataset

4. Consider the following scenario: You are building a multimodal system for autonomous driving that uses both camera images and LiDAR data to perceive the environment. The LiDAR data is sparse and noisy, while the camera images are rich in visual details but can be affected by lighting conditions. Which of the following fusion strategies is MOST robust and effective for combining these two modalities?

A) Use an early fusion approach where the features extracted from each modality are combined at an early stage of the processing pipeline, allowing the model to learn cross-modal interactions.
B) Rely solely on the LiDAR data and discard the camera images to avoid lighting problems.
C) Train separate models for each modality and combine their predictions at the end using a simple averaging scheme.
D) Fuse the raw sensor data directly by concatenating the LiDAR point cloud and the image pixels.
E) Rely solely on the camera images and discard the LiDAR data to simplify the system.

5. Which of the following techniques are MOST effective for improving the energy efficiency of a large-scale Generative A1 model during inference, while minimizing performance degradation?

A) Model quantization (e.g., INT8)
B) Knowledge distillation to a smaller model
C) Pruning (removing less important weights)
D) Increasing the batch size significantly
E) Gradient accumulation

Solutions: