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NVIDIA Generative AI Multimodal Sample Questions:

1. You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

A) Deploying Riva on CPU using multiprocessing.
B) Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.
C) Deploying Riva on a single CPU core with a small batch size.
D) Deploying Riva on a single GPU with a large batch size.
E) Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.

2. Consider a scenario where you are building a multimodal model to generate realistic indoor scenes. You have access to text descriptions of the scene, 3D models of furniture, and ambient sound recordings. Which of the following loss functions would be most appropriate to ensure coherence and realism in the generated scenes?

A) Mean Squared Error (MSE) between the generated image and a reference image.
B) KL Divergence loss between the generated sound and the input text.
C) Cross-entropy loss for classifying different object categories in the scene.
D) Cosine similarity loss between the generated image and the input 3D models.
E) A combination of adversarial loss (GAN) to ensure realism, a perceptual loss to match high-level features, and a semantic consistency loss to align the generated image with the input text description.

3. You are working with a dataset of handwritten digits and training a Variational Autoencoder (VAE) to generate new digits. After training, you observe that the generated digits are blurry and lack sharp details. Which of the following modifications could potentially improve the quality of the generated digits in your VAE?

A) Using a simpler decoder architecture.
B) Decreasing the dimensionality of the latent space.
C) Reducing the weight of the KL divergence term in the VAE loss function.
D) Increasing the capacity of the encoder and decoder networks (e.g., adding more layers or neurons).
E) Increasing the weight of the KL divergence term in the VAE loss function.

4. You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

A) Differences in lighting conditions influencing facial expression recognition.
B) Variations in background noise affecting audio quality.
C) Synchronization issues between audio and video streams.
D) Subjectivity in emotional expression across cultures and individuals.
E) All of the above.

5. You're developing a system that translates spoken language into sign language animations. Which of the following losses would be MOST suitable for training the model to generate realistic and accurate sign language sequences from speech input?

A) A combination of MSE loss for joint positions and a temporal smoothness loss to encourage smooth transitions between sign language poses.
B) Mean Squared Error (MSE) loss between the predicted joint positions of the sign language character and the ground truth joint positions.
C) Cosine Similarity loss between audio embeddings and sign language animation embeddings.
D) Cross-entropy loss between the predicted sign language sequence and the ground truth sequence.
E) Binary Cross entropy to classify the output sign animation-

Solutions: