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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) 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.
B) Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.
C) 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.
D) Both multimodal and unimodal models have an equal risk of gradient vanishing, as the optimization process is independent of the number of modalities.

2. Which technique is commonly used to speed up AI model training and inference on hardware accelerators?

A) Dropout
B) Data augmentation
C) Model enlargement
D) Quantization

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

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

4. What characteristic of autoencoders makes them suitable for anomaly detection?

A) Their ability to classify images with high accuracy.
B) Their function in enhancing the quality of images.
C) Their capacity to learn a compressed representation of the data.
D) Their capability to predict future outcomes based on past data.

5. You have been given a dataset with missing values. What is the first step you should take with the data?

A) Analyze the patterns and distribution of missing values.
B) Fill in the missing values with a default value.
C) Remove the rows with missing values.
D) Remove the columns with missing values.

Solutions: