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Updated: Jul 22, 2026
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | Generative AI Multimodal Certification Exam |
| Exam Number: | NCA-GENM |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Available Languages: | English, Chinese |
| Exam Format: | Multiple choice |
| Certificate Validity Period: | 2 years |
| Exam Price: | $125 USD |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 50-60 |
| Passing Score: | Not publicly disclosed |
| Recommended Training: | Fundamentals of Generative AI Getting Started With Deep Learning |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCA-GENM Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | Basic understanding of generative AI concepts and principles |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal-associate/ |
NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications - Best practices for building and maintaining systems |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Experimentation | 25% | - Experiment design and methodology - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation |
| Multimodal Data | 15% | - Multimodal model architectures and integration - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
NVIDIA Generative AI Multimodal Sample Questions:
1. Which technique involves leveraging pre-trained models to achieve efficient results with less data and computation?
A) Prompt engineering
B) Neural network integration
C) Transfer learning
D) State management and composition
2. What is a common method to reduce the computational cost of deep learning models during inference?
A) By replacing activation functions in some neurons with simpler ones.
B) Adding more convolutional filters.
C) Increasing the batch size.
D) Pruning weights or neurons.
3. In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?
A) K-means clustering
B) Generative adversarial network (GAN)
C) Decision tree
D) Support vector machine (SVM)
4. You are conducting an experiment to evaluate the performance of different AI models. What is the purpose of AI model evaluation?
A) To determine the ethical implications of AI model usage.
B) To analyze the cost-effectiveness of AI model development.
C) To determine the best AI model architecture.
D) To study the impact of AI models on human behavior.
5. Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?
A) Mean Opinion Score (MOS)
B) F1 Score
C) Word Error Rate (WER)
D) CTC Loss
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: C |
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