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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. What is the purpose of a kernel in a Convolutional Neural Network (CNN)?

A) To calculate the loss function.
B) To perform convolution operations on input data.
C) To classify the data into different categories.
D) To normalize the input data.

2. In experimentation, how does data augmentation contribute to improving model accuracy?

A) It reduces the complexity of the model, making it easier to train and evaluate.
B) It has no impact on model accuracy and is primarily used for data visualization purposes.
C) It helps in increasing the size of the dataset, leading to better generalization of the model.
D) It improves the interpretability of the model by providing additional insights into the data.

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

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

4. Which of the following is a disadvantage of the ReLU activation function?

A) It is computationally expensive.
B) It can cause dead neurons.
C) It is prone to vanishing gradient problem.
D) It is not suitable for deep neural networks.

5. What is the significance of A/B testing in ML software engineering?

A) A/B testing is used to measure the impact of changes in the user interface of a ML application.
B) A/B testing helps in evaluating the performance and effectiveness of different machine learning models.
C) A/B testing is irrelevant in ML software engineering.
D) A/B testing helps in optimizing the hyperparameters of a machine learning model.

Solutions: