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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

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

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

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

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

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

5. What is the purpose of the cuDNN library?

A) To generate images from English text-prompts using CLIP.
B) To implement GPU-accelerated data preparation and feature extraction.
C) To optimize deep neural network computations on NVIDIA GPUs.
D) To measure GPU usage and other metrics with Prometheus.

Solutions: