Download NVIDIA : NCA-GENM Questions & Answers as PDF & Test Software
Updated: Oct 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 |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Exam Duration: | 60 minutes |
| Exam Price: | $125 USD |
| Real Exam Qty: | 50-60 |
| Available Languages: | Chinese, English |
| Exam Format: | Multiple choice |
| Passing Score: | Not publicly disclosed |
| Certificate Validity Period: | 2 years |
| 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 |
|---|---|---|
| Topic 1: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 2: 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 |
| Topic 3: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 4: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Topic 5: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
| Topic 6: Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
| Topic 7: 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:
In machine learning, what is the purpose of data normalization?
- A. To reduce the dimensionality of the dataset.
- B. To remove irrelevant data from the dataset.
- C. To increase the complexity of the dataset.
- D. To convert data into a specific format for easier analysis.
Correct Answer: D 🗳️
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?
- A. It requires AI systems to be developed with an ethical consideration for societal impacts.
- B. It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
- C. It ensures that AI systems are transparent in their decision-making processes.
- D. It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
Correct Answer: B 🗳️
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 enhance datasets through data augmentation for text-to-image generation.
- C. CLIP is used to generate image captions from textual input.
- D. CLIP is used to convert textual input into image embeddings.
Correct Answer: A 🗳️
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
What role does 'late fusion' play in multimodal machine learning?
- A. It refers to the process of combining multiple modalities at the feature level.
- B. It refers to the process of combining multiple modalities at the training stage.
- C. It refers to the process of combining multiple modalities at the preprocessing stage.
- D. It refers to the process of combining multiple modalities at the decision level.
Correct Answer: D 🗳️
Explanation: Only visible for TestSimulate members. You can sign-up / login (it's free).
In multimodal machine learning, what does 'early fusion' refer to?
- A. Training separate models for each modality and then combining their predictions.
- B. Implementing the model in the early stages of development of the ML solution.
- C. Ignoring certain modalities and only using one modality for analysis and prediction.
- D. Integrating different modalities at the beginning of the model pipeline.
Correct Answer: D 🗳️
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