NVIDIA NCA-GENM : NVIDIA Generative AI Multimodal

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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. For building a zero-shot image classification pipeline, what could be a crucial step in the process?

A) Manually labeling each image in the dataset for precise classification.
B) Designing an algorithm to replace the need for textual descriptions in the classification process.
C) Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.
D) Focusing on enhancing the resolution and quality of images before classification.

2. What does mixed-precision training refer to?

A) Training a model using different types of data, such as text, images, audio, time series, and geospatial 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 incomplete or missing information from different modalities.
D) Training a model using diverse data types while addressing challenges related to missing or incomplete information.

3. What does 'kernel fusion' refer to in the context of AI model optimization?

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

4. In convolutional neural networks, we may use padding in both convolution and transposed convolution.
Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.

A) Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
B) Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
C) Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.
D) Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
E) In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.

5. Which framework is used for conversational AI models development?

A) NVIDIA Clara
B) NVIDIA NeMo
C) NVIDIA Metropolis
D) NVIDIA DeepStream

Solutions: