NVIDIA Generative AI Multimodal - NCA-GENM

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

2. Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?

A) Word Error Rate (WER)
B) Mean Opinion Score (MOS)
C) F1 Score
D) CTC Loss

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

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

4. In the transformer architecture, what is the purpose of positional encoding?

A) To encode the semantic meaning of each token in the input sequence.
B) To add information about the order of each token in the input sequence.
C) To encode the importance of each token in the input sequence.
D) To remove redundant information from the input sequence.

5. What characteristic of autoencoders makes them suitable for anomaly detection?

A) Their function in enhancing the quality of images.
B) Their ability to classify images with high accuracy.
C) Their capability to predict future outcomes based on past data.
D) Their capacity to learn a compressed representation of the data.

Solutions: