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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

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

2. How is the optimization of a multimodal model different from a unimodal model in terms of gradient vanishing?

A) Both multimodal and unimodal models have an equal risk of gradient vanishing, as the optimization process is independent of the number of modalities.
B) Multimodal models have a higher risk of gradient vanishing compared to unimodal models, as the combination of multiple modalities increases the complexity of the model architecture.
C) Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.
D) Unimodal models have a higher risk of gradient vanishing compared to multimodal models, as the focus on a single modality allows for better gradient flow and stability.

3. What is a common method to reduce the computational cost of deep learning models during inference?

A) Adding more convolutional filters.
B) Increasing the batch size.
C) By replacing activation functions in some neurons with simpler ones.
D) Pruning weights or neurons.

4. Which framework is used for conversational AI models development?

A) NVIDIA DeepStream
B) NVIDIA Clara
C) NVIDIA Metropolis
D) NVIDIA NeMo

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

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

Solutions: