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Updated: Aug 29, 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 Duration: | 60 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | Chinese, English |
| Exam Format: | Multiple choice |
| Real Exam Qty: | 50-60 |
| Passing Score: | Not publicly disclosed |
| Exam Price: | $125 USD |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| 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% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results - Interpretation of generative AI outputs |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| 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 |
| 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 |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
A. Quantization
B. Model enlargement
C. Data augmentation
D. Dropout
Question 2
Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?
A. Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
B. Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.
C. Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
D. More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
Question 3
What advantage does multimodal learning have over unimodal learning?
A. It is more reliable than unimodal learning.
B. It can capture more complex patterns and relationships in data.
C. It is easier to collect multimodal data than unimodal data.
D. It requires fewer data samples for learning.
Question 4
Which of the following best describes the role of machine learning in handling multimodal data?
A. To eliminate the need for human intervention in data analysis.
B. To enable models to learn from and interpret diverse data types.
C. To reduce the amount of data needed for accurate predictions.
D. To focus on textual data analysis.
Question 5
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. Linear Regression
B. Logistic Regression
C. K-Means Clustering
D. Convolutional Neural Network (CNN)
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: D |
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