NVIDIA NCA-GENM

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

Question #1
What does mixed-precision training refer to?

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

Question #2
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. Pie chart
B. Bar chart
C. Scatter plot
D. Line chart

Question #3
What role does 'late fusion' play in multimodal machine learning?

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

Question #4
What is the significance of A/B testing in ML software engineering?

A. A/B testing helps in evaluating the performance and effectiveness of different machine learning models.
B. A/B testing is irrelevant in ML software engineering.
C. A/B testing helps in optimizing the hyperparameters of a machine learning model.
D. A/B testing is used to measure the impact of changes in the user interface of a ML application.

Question #5
What does 'kernel fusion' refer to in the context of AI model optimization?

A. Using kernel functions to optimize model hyperparameters.
B. Optimizing model inference by reducing the number of computations by pruning.
C. Combining multiple kernels into a single kernel for faster computation.
D. Applying multiple layers of kernels to improve model accuracy.

Solutions: