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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

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

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

2. What role does 'late fusion' play in multimodal machine learning?

A) It refers to the process of combining multiple modalities at the training stage.
B) It refers to the process of combining multiple modalities at the decision level.
C) It refers to the process of combining multiple modalities at the preprocessing stage.
D) It refers to the process of combining multiple modalities at the feature level.

3. In LLM evaluation, what does "zero-shot learning" refer to?

A) The model's performance after extensive training
B) A technique to reduce training time to zero
C) The model's ability to perform tasks it has not been explicitly trained on
D) The model's ability to learn from zero examples

4. Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?

A) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
B) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
C) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
D) Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.

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

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

Solutions: