NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI Practice Questions: Core Machine Learning and AI Knowledge Domain

Published: July 30, 2025 | 20 min read

Test your NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI knowledge with 10 practice questions from the Core Machine Learning and AI Knowledge domain. Includes detailed explanations and answers.

NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI Practice Questions

Master the Core Machine Learning and AI Knowledge Domain

Test your knowledge in the Core Machine Learning and AI Knowledge domain with these 10 practice questions. Each question is designed to help you prepare for the NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI certification exam with detailed explanations to reinforce your learning.

Question 1

Which NVIDIA technology can be used to efficiently train large-scale multimodal AI models using distributed computing?

A) NVIDIA CUDA

B) NVIDIA NCCL

C) NVIDIA CUDNN

D) NVIDIA DIGITS

Show Answer & Explanation

Correct Answer: B

Explanation: NVIDIA NCCL (NVIDIA Collective Communications Library) is designed to efficiently train large-scale multimodal AI models using distributed computing. It provides fast communication primitives for multi-GPU and multi-node setups. CUDA is for general GPU computing, CUDNN is a deep learning library, and DIGITS is a web application for training deep learning models.

Question 2

What is the primary role of neural networks in multimodal AI systems?

A) To perform linear regression on large datasets

B) To integrate and learn from multiple data types simultaneously

C) To execute SQL queries for data retrieval

D) To provide data encryption for secure transmission

Show Answer & Explanation

Correct Answer: B

Explanation: The primary role of neural networks in multimodal AI systems is to integrate and learn from multiple data types (such as images, text, and audio) simultaneously, enabling the system to make more informed and comprehensive predictions. Option A refers to a basic statistical method, C is related to database management, and D pertains to data security, none of which are directly related to the core function of neural networks in multimodal AI.

Question 3

In multimodal AI systems, what is the primary role of attention mechanisms?

A) To preprocess data before it is fed into the model.

B) To enhance the model's ability to focus on relevant parts of the input data.

C) To reduce the dimensionality of input data.

D) To increase the model's training speed.

Show Answer & Explanation

Correct Answer: B

Explanation: Attention mechanisms are used to enhance the model's ability to focus on the most relevant parts of the input data, which is crucial in multimodal systems where different data types are integrated. This allows the model to prioritize certain features over others based on context. They do not preprocess data (A), reduce dimensionality (C), or directly increase training speed (D).

Question 4

Which algorithm is most appropriate for handling sequential data in multimodal AI applications?

A) Convolutional Neural Networks (CNNs)

B) Recurrent Neural Networks (RNNs)

C) Support Vector Machines (SVMs)

D) K-Means Clustering

Show Answer & Explanation

Correct Answer: B

Explanation: Recurrent Neural Networks (RNNs) are specifically designed to handle sequential data, making them ideal for applications involving time-series or sequence prediction in multimodal AI. CNNs are typically used for spatial data like images, SVMs are for classification tasks, and K-Means is a clustering algorithm.

Question 5

Which algorithm is most effective for training a neural network that processes both audio and visual data simultaneously?

A) K-Means Clustering

B) Convolutional Neural Networks (CNN)

C) Recurrent Neural Networks (RNN)

D) Multimodal Transformers

Show Answer & Explanation

Correct Answer: D

Explanation: Multimodal Transformers are specifically designed to handle multiple types of data inputs, such as audio and visual data, simultaneously. They leverage attention mechanisms to effectively integrate and process diverse data modalities. CNNs are primarily for image data, RNNs for sequential data, and K-Means for clustering.

Question 6

In the context of neural networks, which loss function is most appropriate for training a multimodal model that combines image and text data for classification tasks?

A) Mean Squared Error (MSE)

B) Cross-Entropy Loss

C) Hinge Loss

D) Huber Loss

Show Answer & Explanation

Correct Answer: B

Explanation: Cross-Entropy Loss is commonly used for classification tasks, including those involving multimodal data like images and text. It measures the performance of a classification model whose output is a probability value between 0 and 1. MSE is used for regression, Hinge Loss for SVMs, and Huber Loss for robust regression.

Question 7

When developing a neural network model for a multimodal AI system, which deep learning framework would be most beneficial for leveraging NVIDIA GPUs?

A) TensorFlow

B) Scikit-learn

C) XGBoost

D) LightGBM

Show Answer & Explanation

Correct Answer: A

Explanation: TensorFlow is a powerful deep learning framework that is highly optimized for running on NVIDIA GPUs, making it suitable for developing complex multimodal AI systems. Scikit-learn is not primarily used for deep learning, and XGBoost and LightGBM are gradient boosting frameworks, not deep learning frameworks.

Question 8

Which NVIDIA framework is specifically designed for building and deploying GPU-accelerated AI applications, including those that are multimodal?

A) NVIDIA DIGITS

B) NVIDIA Clara

C) NVIDIA Triton Inference Server

D) NVIDIA NeMo

Show Answer & Explanation

Correct Answer: D

Explanation: NVIDIA NeMo is a framework specifically designed for building and deploying GPU-accelerated conversational AI applications, including multimodal models that integrate text, audio, and visual data. NVIDIA DIGITS is more focused on image classification, Clara is for healthcare applications, and Triton Inference Server is for serving models in production environments.

Question 9

In NVIDIA's ecosystem, which tool would you use to accelerate the training of a multimodal neural network?

A) NVIDIA DIGITS

B) NVIDIA CUDA

C) NVIDIA Nsight

D) NVIDIA GeForce Experience

Show Answer & Explanation

Correct Answer: B

Explanation: NVIDIA CUDA is a parallel computing platform and application programming interface model that allows developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing, which accelerates the training of neural networks, including multimodal ones. NVIDIA DIGITS is a deep learning training system but not as directly related to GPU acceleration as CUDA. NVIDIA Nsight is a debugging tool, and GeForce Experience is for gaming optimization.

Question 10

What is the role of neural network embeddings in multimodal AI systems?

A) To increase the dataset size for training

B) To convert multimodal data into a common representation space

C) To enhance the visual quality of images

D) To reduce the computational power required for model training

Show Answer & Explanation

Correct Answer: B

Explanation: Embeddings in neural networks are used to map data from different modalities into a common representation space, facilitating the integration and processing of multimodal data. They do not increase dataset size, enhance image quality, or reduce computational power directly.

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About NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI Certification

The NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI certification validates your expertise in core machine learning and ai knowledge and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.