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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?

A) Histogram
B) Pie chart
C) Heatmap
D) Box plot

2. Hyperparameter tuning is used for what purpose in machine learning experimentation?

A) Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.
B) Selecting the best ML algorithm for a given task.
C) Adjusting the weights and biases of a neural network to optimize its performance.
D) Collecting and preprocessing data to improve the accuracy of the model.

3. 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) 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.
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) 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.

4. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?

A) Logistic Regression
B) Linear Regression
C) Convolutional Neural Network (CNN)
D) K-Means Clustering

5. What does 'kernel fusion' refer to in the context of AI model optimization?

A) Applying multiple layers of kernels to improve model accuracy.
B) Using kernel functions to optimize model hyperparameters.
C) Combining multiple kernels into a single kernel for faster computation.
D) Optimizing model inference by reducing the number of computations by pruning.

Solutions: