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NVIDIA Generative AI Multimodal Sample Questions:

1. You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?

A) Combining A, B, and C.
B) Using generative adversarial networks (GANs) to generate new furniture images from the existing dataset.
C) Synthesizing new text descriptions using paraphrasing and back-translation techniques.
D) Randomly cropping and rotating the existing furniture images.
E) Focusing solely on increasing the size of the text dataset and ignoring image augmentation.

2. You are building a system that identifies objects in images based on spoken commands. You have trained a model but notice that it performs poorly when the spoken command contains synonyms or paraphrases of the training data. Which of the following techniques would BEST address this issue?

A) Increasing the size of the training dataset.
B) Employing a word embedding model (e.g., Word2Vec, GloVe) or contextual embeddings (e.g., BERT) to represent the spoken commands, allowing the model to generalize to semantically similar phrases.
C) Using data augmentation techniques such as rotating and scaling the images.
D) Reducing the learning rate of the model.
E) Simplifying the spoken commands to use only a limited vocabulary.

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

4. Which of the following techniques can be used to improve the factual accuracy of text generated by a large language model?

A) Increasing the model size and training it on more data.
B) Always using the same prompt, regardless of the desired output.
C) Fine-tuning the model on a dataset of factually correct information.
D) Applying a temperature of 0 during text generation.
E) Using retrieval-augmented generation (RAG) to ground the model's knowledge in external sources.

5. You are tasked with building a system that generates realistic images based on both textual descriptions and a semantic segmentation map. The segmentation map provides spatial information about the objects present in the scene. Which of the following generative architectures is MOST appropriate for this multimodal task?

A) Autoregressive model like PixelCNN
B) Conditional Generative Adversarial Network (cGAN) with both text and segmentation map as conditions.
C) Diffusion model without conditioning
D) Variational Autoencoder (VAE)
E) Vanilla Generative Adversarial Network (GAN)

Solutions: