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Updated: Sep 21, 2026
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | Generative AI Multimodal Certification Exam |
| Exam Number: | NCA-GENM |
| Certificate Validity Period: | 2 years |
| Exam Price: | $125 USD |
| Available Languages: | English, Chinese |
| Exam Format: | Multiple choice |
| Real Exam Qty: | 50-60 |
| Exam Duration: | 60 minutes |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| 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 |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI |
NVIDIA Generative AI Multimodal Sample Questions:
What is the purpose of the cuDNN library?
- A. To generate images from English text-prompts using CLIP.
- B. To measure GPU usage and other metrics with Prometheus.
- C. To optimize deep neural network computations on NVIDIA GPUs.
- D. To implement GPU-accelerated data preparation and feature extraction.
Correct Answer: C 🗳️
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How does CLIP understand the content of both text and images?
- A. Using contrastive learning to match images with text descriptions.
- B. By converting text and images into a frequency domain for comparison.
- C. Through a database of predefined images with their descriptions.
- D. By translating images into text and comparing them with the prompt.
Correct Answer: A 🗳️
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You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?
- A. K-Means Clustering
- B. Convolutional Neural Network (CNN)
- C. Linear Regression
- D. Logistic Regression
Correct Answer: B 🗳️
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For building a zero-shot image classification pipeline, what could be a crucial step in the process?
- A. Focusing on enhancing the resolution and quality of images before classification.
- B. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.
- C. Designing an algorithm to replace the need for textual descriptions in the classification process.
- D. Manually labeling each image in the dataset for precise classification.
Correct Answer: B 🗳️
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Which of the following is a disadvantage of the ReLU activation function?
- A. It can cause dead neurons.
- B. It is prone to vanishing gradient problem.
- C. It is computationally expensive.
- D. It is not suitable for deep neural networks.
Correct Answer: A 🗳️
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