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NVIDIA Generative AI Multimodal Sample Questions:

1. You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?

A) Using a simpler loss function during training.
B) Switching to a larger model architecture with more parameters.
C) Increasing the batch size during inference to better utilize GPU resources.
D) Removing dropout layers from the model.
E) Implementing TensorRT for model optimization and quantization.

2. Which of the following are key challenges specific to training multimodal models compared to unimodal models? (Select TWO)

A) The lack of readily available pre-trained models for different modalities.
B) The relative simplicity of unimodal model architectures.
C) Increased computational cost due to processing multiple data types.
D) The difficulty of evaluating the performance of multimodal models.
E) Aligning and fusing information from different modalities with potentially different representations and noise characteristics.

3. Consider the following code snippet intended to generate an image embedding using CLIP. What is the most likely reason for the 'RuntimeErroN?

A) The image tensor does not require gradient calculation.
B) The CLIP model was not properly loaded onto the GPIJ.
C) The image is not in RGB format.
D) The image pixel values are not normalized correctly.
E) The image size is not compatible with the CLIP model's input requirements.

4. You are building a real-time multimodal system that processes live video and audio streams to detect potentially dangerous situations. Latency is a critical constraint. Which of the following strategies is MOST important to minimize latency in this system?

A) Using a large batch size during inference to maximize GPU utilization.
B) Converting the video stream to text transcripts before processing.
C) Optimizing the model architecture for efficient computation, using techniques like model quantization, knowledge distillation, and reducing the number of layers.
D) Using a very deep neural network to achieve the highest possible accuracy, regardless of latency.
E) Employing extensive data augmentation during training.

5. Given the following Python code snippet using Pandas, which is intended to filter rows where the 'price' column is greater than 100 and the 'quantity' column is less than 5, identify the correct approach to achieve this:

A)

B)

C)

D)

E)

Solutions: