NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI Practice Questions: Software Development 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 Software Development domain. Includes detailed explanations and answers.

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

Master the Software Development Domain

Test your knowledge in the Software Development 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 Python library would you use to create a multimodal AI application that requires real-time video processing on NVIDIA hardware?

A) OpenCV

B) NumPy

C) Matplotlib

D) Pandas

Show Answer & Explanation

Correct Answer: A

Explanation: OpenCV is a library primarily used for computer vision tasks and supports real-time video processing. It can be optimized to run on NVIDIA GPUs using CUDA. NumPy is used for numerical computations, Matplotlib is for data visualization, and Pandas is for data manipulation. None of these libraries are specifically designed for real-time video processing like OpenCV.

Question 2

In the context of documenting a multimodal AI system, which tool would you use to create interactive and shareable Python reports?

A) Jupyter Notebook

B) PyCharm

C) Eclipse

D) Visual Studio Code

Show Answer & Explanation

Correct Answer: A

Explanation: Jupyter Notebook allows for creating interactive and shareable reports with live code, visualizations, and narrative text, making it ideal for documenting AI experiments. PyCharm (B), Eclipse (C), and Visual Studio Code (D) are IDEs that do not inherently provide the interactive reporting capabilities of Jupyter Notebook.

Question 3

For a Python application utilizing NVIDIA's Riva for speech recognition, which software development practice enhances maintainability?

A) Embedding all configurations directly in the code.

B) Using environment variables for configuration settings.

C) Hardcoding model paths within the application.

D) Avoiding the use of virtual environments.

Show Answer & Explanation

Correct Answer: B

Explanation: Using environment variables for configuration settings enhances maintainability by separating configuration from code, allowing for easier updates and deployments. Option B is correct because it supports flexible and secure configuration management. Option A is incorrect because embedding configurations in code reduces flexibility. Option C is incorrect because hardcoding paths can lead to maintenance challenges. Option D is incorrect because virtual environments help manage dependencies effectively.

Question 4

In a multimodal AI project using NVIDIA's Triton Inference Server, which of the following is essential for integrating a Python-based model into the deployment pipeline?

A) Convert the model to ONNX format before deployment.

B) Write a custom Python script to handle input/output preprocessing.

C) Ensure the model is trained using NVIDIA's RAPIDS library.

D) Implement model quantization using TensorRT.

Show Answer & Explanation

Correct Answer: B

Explanation: Triton Inference Server supports Python models by allowing custom Python scripts for input/output preprocessing, which is crucial for handling multimodal data. Option A is incorrect because ONNX conversion is not mandatory for Python models. Option C is irrelevant to the integration process, as RAPIDS is not required for deployment. Option D, while useful for optimization, is not directly related to integrating a Python model.

Question 5

Which of the following practices is crucial for ensuring efficient system integration when deploying a multimodal AI model using NVIDIA technologies?

A) Using a single-threaded processing model.

B) Implementing a microservices architecture.

C) Storing data in a local SQLite database.

D) Developing the entire system in Java.

Show Answer & Explanation

Correct Answer: B

Explanation: Implementing a microservices architecture allows for scalable and efficient system integration, especially when deploying complex multimodal AI models. Option B is correct because microservices enable modular development and easy integration with NVIDIA technologies. Option A is incorrect because single-threaded models can limit performance. Option C is not ideal for large-scale deployments due to SQLite's limitations. Option D is incorrect because while Java is a viable language, Python is more commonly used for NVIDIA AI solutions.

Question 6

You are tasked with documenting a multimodal AI system developed using NVIDIA technologies. Which tool would you use to generate API documentation directly from Python code?

A) Sphinx

B) Jupyter Notebook

C) PyCharm

D) Microsoft Word

Show Answer & Explanation

Correct Answer: A

Explanation: Sphinx is a tool that makes it easy to create intelligent and beautiful documentation, especially for Python projects. It can generate documentation directly from the code, including API documentation. Jupyter Notebook is an interactive computing environment, PyCharm is an IDE for Python development, and Microsoft Word is a word processor, none of which are specifically used for generating API documentation.

Question 7

In a multimodal AI project using NVIDIA's DeepStream SDK, which Python library is best suited for integrating custom AI models for video analytics?

A) OpenCV

B) NumPy

C) PyTorch

D) TensorRT

Show Answer & Explanation

Correct Answer: D

Explanation: TensorRT is NVIDIA's SDK for high-performance deep learning inference. It is specifically designed to optimize and deploy AI models efficiently, making it the best choice for integrating custom AI models within the DeepStream SDK for video analytics. OpenCV is used for computer vision tasks, NumPy is for numerical computations, and PyTorch is a framework for building models rather than deploying them.

Question 8

Which NVIDIA technology would you use to integrate real-time voice recognition into a multimodal AI system developed in Python?

A) NVIDIA CUDA

B) NVIDIA Riva

C) NVIDIA Omniverse

D) NVIDIA Clara

Show Answer & Explanation

Correct Answer: B

Explanation: NVIDIA Riva is a GPU-accelerated SDK for building and deploying real-time speech AI applications, making it the ideal choice for integrating voice recognition into a multimodal AI system. NVIDIA CUDA is a parallel computing platform, NVIDIA Omniverse is a collaboration platform for 3D content creation, and NVIDIA Clara is a healthcare application framework.

Question 9

In the context of multimodal AI application development, why is it important to use NVIDIA's Triton Inference Server?

A) It provides a cloud-based training environment

B) It enhances model interpretability

C) It allows deployment of models from different frameworks

D) It is primarily used for data annotation

Show Answer & Explanation

Correct Answer: C

Explanation: NVIDIA's Triton Inference Server is designed to deploy models from different frameworks, such as TensorFlow, PyTorch, and ONNX, in a production environment, making it highly suitable for multimodal AI applications. It does not provide a training environment, enhance model interpretability, or focus on data annotation.

Question 10

In a multimodal AI project using NVIDIA's TensorRT, which of the following Python libraries would you primarily use to integrate and deploy the model efficiently?

A) Pandas

B) NumPy

C) PyTorch

D) ONNX

Show Answer & Explanation

Correct Answer: D

Explanation: The correct answer is D: ONNX. ONNX (Open Neural Network Exchange) is a format for deep learning models that allows interoperability between different AI frameworks. When using NVIDIA's TensorRT, ONNX is commonly used to export models for efficient deployment. Option A (Pandas) is incorrect as it is primarily used for data manipulation and analysis. Option B (NumPy) is incorrect as it is a library for numerical computations. Option C (PyTorch) is incorrect as it is a deep learning framework, but not specifically for deploying models with TensorRT.

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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 software development 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.