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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following tasks can be performed using the transformer LLM encoder model?

A) Image generation
B) Speech recognition
C) Semantic analysis
D) Generating code

2. What is a main application of Triton Inference Server?

A) Triton Server can be used to generate images from pure noise.
B) Triton Server can be used to deploy AI models on the GPU only.
C) Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.
D) Triton Server can be used to deploy neural networks from various frameworks.

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 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) 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.
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. What are some methods to overcome limited throughput between CPU and GPU?

A) Increase the clock speed of the CPU.
B) Increase the number of CPU cores.
C) Upgrade the GPU to a higher-end model.
D) Using techniques like memory pooling.

5. What characteristic of autoencoders makes them suitable for anomaly detection?

A) Their function in enhancing the quality of images.
B) Their capability to predict future outcomes based on past data.
C) Their ability to classify images with high accuracy.
D) Their capacity to learn a compressed representation of the data.

Solutions: