NVIDIA Generative AI Multimodal : NCA-GENM

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. Which technique involves leveraging pre-trained models to achieve efficient results with less data and computation?

A) State management and composition
B) Neural network integration
C) Prompt engineering
D) Transfer learning

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

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

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

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

5. How does the batch size influence VRAM consumption during inference with ML models on GPUs?

A) Increasing or decreasing the batch size has the same impact on VRAM consumption.
B) The batch size has no impact on VRAM consumption during inference.
C) Decreasing the batch size reduces VRAM consumption.
D) Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.

Solutions: