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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. In the context of multimodal machine learning, what does 'data fusion' refer to?

A) Removing missing or incomplete information from different modalities.
B) Evaluating the quality of diverse data types in multimodal machine learning.
C) Combining different modalities of data into a single representation.
D) Separating different modalities of data into distinct representations.

2. You are working with a large dataset and want to visualize the distribution of a continuous variable. Which type of data visualization would be most appropriate?

A) Bar chart
B) Histogram chart
C) Pie chart
D) Line chart

3. In the transformer architecture, what is the purpose of positional encoding?

A) To encode the semantic meaning of each token in the input sequence.
B) To remove redundant information from the input sequence.
C) To encode the importance of each token in the input sequence.
D) To add information about the order of each token in the input sequence.

4. You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?

A) Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
B) The data used to train the AI system may not be representative of the population it is intended to serve.
C) Bias in data can only be addressed after the AI system has been deployed.
D) Bias in data is irrelevant as long as the AI system produces accurate predictions.

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 increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
B) 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.
C) 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.
D) 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.
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: