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Updated: Aug 02, 2026
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | Generative AI Multimodal Certification Exam |
| Exam Number: | NCA-GENM |
| Real Exam Qty: | 50-60 |
| Passing Score: | Not publicly disclosed |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Exam Format: | Multiple choice |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Chinese |
| Exam Price: | $125 USD |
| 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 |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
| Topic 2: Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Topic 3: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Topic 4: 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 |
| Topic 5: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Topic 6: Performance Optimization | 10% | - Scalability and deployment considerations - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms |
| Topic 7: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
NVIDIA Generative AI Multimodal Sample Questions:
1. Which of the following best describes the role of the Hugging Face model repository in ML software development?
A) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
B) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
C) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
D) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
2. In a multimodal machine learning context, how are different modalities usually linked to each other?
A) Different modalities are not linked to each other in a multimodal machine learning context.
B) Different modalities are linked through a shared representation that captures the relationships between the modalities.
C) Different modalities are linked through random connections.
D) Different modalities are linked through separate models that are ensembled by tree-based models.
3. Which of the following is a disadvantage of the ReLU activation function?
A) It is prone to vanishing gradient problem.
B) It can cause dead neurons.
C) It is computationally expensive.
D) It is not suitable for deep neural networks.
4. What does 'modality alignment' refer to?
A) The integration of pretrained models to perform custom tasks involving different types of data.
B) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
C) Addressing challenges related to missing or incomplete information across different modalities.
D) Aligning different modalities within multimodal data to ensure meaningful connections and associations.
5. What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
A) CLIP provides a common embedding space for both the textual and image modalities.
B) CLIP is used to generate image captions from textual input.
C) CLIP is used to enhance datasets through data augmentation for text-to-image generation.
D) CLIP is used to convert textual input into image embeddings.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A |
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