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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. In large-language models, what is the purpose of the attention mechanism?

A) To measure the importance of the words in the output sequence.
B) To determine the order in which words are generated.
C) To capture the order of the words in the input sequence.
D) To assign weights to each word in the input sequence.

2. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

3. What is the correct order of steps in an ML project?

A) Data collection, Data preprocessing, Model training, Model evaluation
B) Model evaluation, Data collection, Data preprocessing, Model training
C) Data preprocessing, Data collection, Model training, Model evaluation
D) Model evaluation, Data preprocessing, Model training, Data collection

4. 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.

5. 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) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
B) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
C) 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.
D) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.

Solutions: