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Updated: Aug 20, 2026
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | Generative AI Multimodal Certification Exam |
| Exam Number: | NCA-GENM |
| Exam Price: | $125 USD |
| Passing Score: | Not publicly disclosed |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 50-60 |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Exam Format: | Multiple choice |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Chinese |
| Recommended Training: | Getting Started With Deep Learning Fundamentals of Generative AI |
| 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 |
|---|---|---|
| Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| 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 |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
NVIDIA Generative AI Multimodal Sample Questions:
1. How does CLIP understand the content of both text and images?
A) By converting text and images into a frequency domain for comparison.
B) Through a database of predefined images with their descriptions.
C) By translating images into text and comparing them with the prompt.
D) Using contrastive learning to match images with text descriptions.
2. Which of the following is a component of the Content Authenticity Initiative?
A) Content credential
B) Ethical AI development
C) Data encryption
D) Content validity
3. What is the purpose of a kernel in a Convolutional Neural Network (CNN)?
A) To normalize the input data.
B) To classify the data into different categories.
C) To calculate the loss function.
D) To perform convolution operations on input data.
4. Which framework is used for conversational AI models development?
A) NVIDIA Clara
B) NVIDIA DeepStream
C) NVIDIA Metropolis
D) NVIDIA NeMo
5. 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) Support vector machine (SVM)
D) Decision tree
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
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