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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. 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) 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.
C) Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.
D) 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.

2. Which of the following is a component of the Content Authenticity Initiative?

A) Ethical AI development
B) Content credential
C) Content validity
D) Data encryption

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

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

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) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
B) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
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. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?

A) Linear Regression
B) Logistic Regression
C) Convolutional Neural Network (CNN)
D) K-Means Clustering

Solutions: