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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. 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) Remove the columns with missing values.
C) Remove the rows with missing values.
D) Fill in the missing values with a default value.

2. What does 'modality alignment' refer to?

A) Addressing challenges related to missing or incomplete information across different modalities.
B) Aligning different modalities within multimodal data to ensure meaningful connections and associations.
C) The integration of pretrained models to perform custom tasks involving different types of data.
D) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.

3. Hyperparameter tuning is used for what purpose in machine learning experimentation?

A) Selecting the best ML algorithm for a given task.
B) Collecting and preprocessing data to improve the accuracy of the model.
C) Adjusting the weights and biases of a neural network to optimize its performance.
D) Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.

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 increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
B) 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.
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 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.

5. In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

A) It ensures that AI systems are transparent in their decision-making processes.
B) It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
C) It requires AI systems to be developed with an ethical consideration for societal impacts.
D) It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

Solutions: