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NVIDIA Generative AI Multimodal Question and Answers

NVIDIA Generative AI Multimodal

Last Update Sep 21, 2026
Total Questions : 56

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Questions 1

Which of the following is a disadvantage of the ReLU activation function?

Options:

A.  

It is computationally expensive.

B.  

It is prone to vanishing gradient problem.

C.  

It is not suitable for deep neural networks.

D.  

It can cause dead neurons.

Discussion 0
Questions 2

In LLM evaluation, what does “zero-shot learning” refer to?

Options:

A.  

The model's ability to learn from zero examples

B.  

A technique to reduce training time to zero

C.  

The model's performance after extensive training

D.  

The model's ability to perform tasks it has not been explicitly trained on

Discussion 0
Questions 3

In multimodal machine learning, what does 'early fusion' refer to?

Options:

A.  

Integrating different modalities at the beginning of the model pipeline.

B.  

Ignoring certain modalities and only using one modality for analysis and prediction.

C.  

Training separate models for each modality and then combining their predictions.

D.  

Implementing the model in the early stages of development of the ML solution.

Discussion 0
Questions 4

Which of the following is a component of the Content Authenticity Initiative?

Options:

A.  

Content validity

B.  

Ethical AI development

C.  

Data encryption

D.  

Content credential

Discussion 0
Questions 5

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
Questions 6

You are conducting an experiment to evaluate the performance of different AI models. What is the purpose of AI model evaluation?

Options:

A.  

To determine the best AI model architecture.

B.  

To determine the ethical implications of AI model usage.

C.  

To study the impact of AI models on human behavior.

D.  

To analyze the cost-effectiveness of AI model development.

Discussion 0
Questions 7

What are some methods to overcome limited throughput between CPU and GPU?

Options:

A.  

Increase the clock speed of the CPU.

B.  

Increase the number of CPU cores.

C.  

Using techniques like memory pooling.

D.  

Upgrade the GPU to a higher-end model.

Discussion 0
Questions 8

What is the purpose of a kernel in a Convolutional Neural Network (CNN)?

Options:

A.  

To perform convolution operations on input data.

B.  

To calculate the loss function.

C.  

To classify the data into different categories.

D.  

To normalize the input data.

Discussion 0
Questions 9

How is the optimization of a multimodal model different from a unimodal model in terms of gradient vanishing?

Options:

A.  

Unimodal models have a higher risk of gradient vanishing compared to multimodal models, as the focus on a single modality allows for better gradient flow and stability.

B.  

Multimodal models have a higher risk of gradient vanishing compared to unimodal models, as the combination of multiple modalities increases the complexity of the model architecture.

C.  

Both multimodal and unimodal models have an equal risk of gradient vanishing, as the optimization process is independent of the number of modalities.

D.  

Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.

Discussion 0
Questions 10

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
Questions 11

Which of the following tasks can be performed using the transformer LLM encoder model?

Options:

A.  

Semantic analysis

B.  

Generating code

C.  

Image generation

D.  

Speech recognition

Discussion 0
Questions 12

In a multimodal machine learning context, how are different modalities usually linked to each other?

Options:

A.  

Different modalities are linked through a shared representation that captures the relationships between the modalities.

B.  

Different modalities are linked through random connections.

C.  

Different modalities are linked through separate models that are ensembled by tree-based models.

D.  

Different modalities are not linked to each other in a multimodal machine learning context.

Discussion 0
Questions 13

You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?

Options:

A.  

Logistic Regression

B.  

K-Means Clustering

C.  

Linear Regression

D.  

Convolutional Neural Network (CNN)

Discussion 0
Questions 14

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.

Options:

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 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.

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.  

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.

Discussion 0
Questions 15

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

Options:

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 remove redundant information from the input sequence.

D.  

To encode the importance of each token in the input sequence.

Discussion 0
Questions 16

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