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NCA-GENM Practice Questions

NVIDIA Generative AI Multimodal

Last Update 3 days ago
Total Questions : 56

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Question # 11

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

Options:

A.  

Adjusting the weights and biases of a neural network to optimize its performance.

B.  

Selecting the best ML algorithm for a given task.

C.  

Collecting and preprocessing data to improve the accuracy of the model.

D.  

Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.

Discussion 0
Question # 12

You have been given a dataset with missing values. What is the first step you should take with the data?

Options:

A.  

Analyze the patterns and distribution of missing values.

B.  

Remove the rows with missing values.

C.  

Fill in the missing values with a default value.

D.  

Remove the columns with missing values.

Discussion 0
Question # 13

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

Options:

A.  

CTC Loss

B.  

F1 Score

C.  

Mean Opinion Score (MOS)

D.  

Word Error Rate (WER)

Discussion 0
Question # 14

You are working with a large dataset and want to visualize the distribution of a continuous variable. Which type of data visualization would be most appropriate?

Options:

A.  

Histogram chart

B.  

Bar chart

C.  

Line chart

D.  

Pie chart

Discussion 0
Question # 15

What is a main application of Triton Inference Server?

Options:

A.  

Triton Server can be used to generate images from pure noise.

B.  

Triton Server can be used to deploy AI models on the GPU only.

C.  

Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.

D.  

Triton Server can be used to deploy neural networks from various frameworks.

Discussion 0
Question # 16

Which technique involves leveraging pre-trained models to achieve efficient results with less data and computation?

Options:

A.  

State management and composition

B.  

Transfer learning

C.  

Prompt engineering

D.  

Neural network integration

Discussion 0
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