NVIDIA Generative AI Multimodal : NCA-GENM

Exam Code: NCA-GENM
Exam Name: NVIDIA Generative AI Multimodal
Updated: Sep 10, 2026 Q & A: 58 Questions and Answers

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Software Development and Engineering	15%	- Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems
Core Machine Learning and AI Knowledge	20%	- Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning
Experimentation	25%	- Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology
Data Analysis and Visualization	10%	- Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results
Trustworthy AI	5%	- Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation
Multimodal Data	15%	- Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation
Performance Optimization	10%	- Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization

NVIDIA Generative AI Multimodal Sample Questions:

Question #1

How does the batch size influence VRAM consumption during inference with ML models on GPUs?

A. Decreasing the batch size reduces VRAM consumption.
B. Increasing or decreasing the batch size has the same impact on VRAM consumption.
C. Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.
D. The batch size has no impact on VRAM consumption during inference.

Answer: A