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NVIDIA Generative AI Multimodal Sample Questions:

1. You are tasked with building a Generative A1 model to generate realistic images of outdoor scenes. The training dataset contains a large number of images with varying lighting conditions, weather conditions, and object compositions. Which data augmentation techniques would be MOST effective in improving the model's robustness and generalization ability?

A) Applying a fixed rotation of 90 degrees to all images.
B) Random cropping and resizing.
C) Color jittering (brightness, contrast, saturation, hue), adding Gaussian noise, and random perspective transformations.
D) Horizontal flipping only.
E) Vertical flipping only

2. You're using a diffusion model to generate high-resolution images. You notice that the generated images often contain artifacts and inconsistencies. Which of the following techniques could help improve the image quality?

A) Employing classifier-free guidance during sampling.
B) Decreasing the number of diffusion steps during sampling.
C) Training with a larger batch size.
D) Using a smaller image size during training.
E) Increasing the number of diffusion steps during training.

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

4. Which of the following techniques are commonly used to address the 'hallucination' problem in generative A1 models, where the model generates content that is factually incorrect or nonsensical? (Select all that apply)

A) Using Reinforcement Learning from Human Feedback (RLHF) to align the model's output with human preferences and factual correctness.
B) Implementing retrieval-augmented generation (RAG), where the model consults an external knowledge source before generating content.
C) Increasing the model size and training dataset.
D) Employing adversarial training techniques to improve the model's robustness to noisy input data.
E) Lowering the temperature parameter during decoding to encourage more conservative and predictable outputs.

5. Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?

A) Facilitating efficient feature extraction and upsampling for detailed image generation.
B) Medical image analysis, such as tumor detection.
C) Image segmentation and pixel-wise classification.
D) Image inpainting and super-resolution tasks.
E) Encoding high-dimensional text data for multimodal embeddings.

Solutions: