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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

Question 1

How is the optimization of a multimodal model different from a unimodal model in terms of gradient vanishing?

A. Both multimodal and unimodal models have an equal risk of gradient vanishing, as the optimization process is independent of the number of modalities.
B. Multimodal models have a higher risk of gradient vanishing compared to unimodal models, as the combination of multiple modalities increases the complexity of the model architecture.
C. Unimodal models have a higher risk of gradient vanishing compared to multimodal models, as the focus on a single modality allows for better gradient flow and stability.
D. Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.

Question 2

What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?

A. CLIP is used to convert textual input into image embeddings.
B. CLIP is used to enhance datasets through data augmentation for text-to-image generation.
C. CLIP provides a common embedding space for both the textual and image modalities.
D. CLIP is used to generate image captions from textual input.

Question 3

Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?

A. Word Error Rate (WER)
B. Mean Opinion Score (MOS)
C. F1 Score
D. CTC Loss

Question 4

You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?

A. Decision Trees
B. K-Means Clustering
C. Linear Regression
D. Convolutional Neural Networks (CNN)

Question 5

In convolutional neural networks, we may use padding in both convolution and transposed convolution.
Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.

A. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
B. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.
C. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
D. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
E. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.

Solutions: