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NVIDIA Generative AI Multimodal Sample Questions:

1. You have a large dataset of images and text descriptions. You want to train a model that can perform both image captioning (generating text from images) and text-to-image generation (generating images from text). What architectural approach is best suited for this multimodal bi-directional task?

A) Train two separate models: one for image captioning and one for text-to-image generation.
B) Use separate encoders for images and text, a shared attention mechanism, and separate decoders for generating text and images.
C) Use a single transformer model with a shared vocabulary and treat both image and text as sequences of tokens.
D) Use a generative adversarial network (GAN) for generating the outputs.
E) Use a shared encoder for both images and text, and separate decoders for generating text and images.

2. Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

A)

B)

C)

D)

E)

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

4. Consider the following Python code snippet using PyTorch. What does this code do in the context of data preprocessing for a Generative AI model?

A)

B)

C)

D)

E)

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

Solutions: