NVIDIA NCA-GENM Exam Questions : NVIDIA Generative AI Multimodal

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?

A) Bar chart
B) Line chart
C) Scatter plot
D) Pie chart

2. What does mixed-precision training refer to?

A) Training a model using incomplete or missing information from different modalities.
B) Training a model using diverse data types while addressing challenges related to missing or incomplete information.
C) Training a model using different types of data, such as text, images, audio, time series, and geospatial information.
D) Training a model using multiple precision levels, such as using both single-precision and double- precision floating-point numbers.

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

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

4. In machine learning, what is the purpose of data normalization?

A) To increase the complexity of the dataset.
B) To reduce the dimensionality of the dataset.
C) To convert data into a specific format for easier analysis.
D) To remove irrelevant data from the dataset.

5. In multimodal machine learning, what does 'early fusion' refer to?

A) Ignoring certain modalities and only using one modality for analysis and prediction.
B) Integrating different modalities at the beginning of the model pipeline.
C) Training separate models for each modality and then combining their predictions.
D) Implementing the model in the early stages of development of the ML solution.

Solutions: