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NVIDIA Generative AI Multimodal Sample Questions:

1. You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)

A) Increase the batch size significantly.
B) Employ data augmentation techniques specifically designed for video data (e.g., temporal jittering, random cropping).
C) Reduce the number of transformer layers in the model.
D) Implement weight decay and dropout regularization.
E) Use a smaller pre-trained model.

2. Consider the following Python code snippet using Triton Inference Server's Python client. The code intends to send a request to a model that expects two input tensors: 'input_image' (shape: [1, 3, 224, 224], datatype: FP32) and 'input_text' (shape: [1 ,], datatype: BYTES). Identify potential issues in this code that could prevent successful inference.

A) The data is not converted to the appropriate NumPy datatype before being sent to Triton.
B) The input data for 'input_text' needs to be encoded to bytes using UTF-8 encoding before being passed to Triton.
C) All of the above.
D) The model name and input/output names must be specified, but they are missing in the code.
E) The function is used incorrectly; it should directly accept the Triton datatype string (e.g., 'FP32').

3. You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?

A) Implementing a retrieval-augmented generation (RAG) approach.
B) Increasing the model size by adding more layers.
C) Removing dropout layers.
D) Reducing the temperature parameter during generation.
E) Training the model on a smaller dataset.

4. You are tasked with building a system that can answer questions based on both an image and a corresponding text description. The image is represented as a feature vector from a CNN, and the text is represented as a sequence of word embeddings from a pre-trained language model. Which architecture would be most suitable for this task?

A) A simple feedforward neural network that concatenates the image and text feature vectors.
B) A combination of a CNN and an LSTM, where the CNN processes the image and the LSTM processes the text independently.
C) Two separate models, one for processing images and another for processing text, with the final answer being chosen based on the higher confidence score.
D) A recurrent neural network (RNN) that processes the text and then uses the final hidden state to attend to the image features.
E) A Transformer-based architecture with cross-attention mechanisms that allow the model to attend to both the image and text features simultaneously.

5. You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?

A) Encode the text prompt using CLIP's text encoder.
B) Fine-tune the CLIP model on your specific image generation task.
C) Calculate the cosine similarity between the image and text embeddings.
D) Resize the generated image to a very high resolution.
E) Encode the generated image using CLIP's image encoder.

Solutions: