NVIDIA NCA-GENM Questions & Answers - in .pdf
- Total Q&A: 58
- Update: Aug 31, 2026
- Price: $59.99
- Vendor: NVIDIA
- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
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- Total Q&A: 58
- Update: Aug 31, 2026
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- Vendor: NVIDIA
- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
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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 |
| Exam Format: | Multiple choice |
| Passing Score: | Not publicly disclosed |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 50-60 |
| Available Languages: | English, Chinese |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) |
| Certificate Validity Period: | 2 years |
| 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: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Topic 2: Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Topic 3: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques |
| Topic 4: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
| Topic 5: Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Topic 6: Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
| Topic 7: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
What advantage does multimodal learning have over unimodal learning?
A. It is easier to collect multimodal data than unimodal data.
B. It requires fewer data samples for learning.
C. It can capture more complex patterns and relationships in data.
D. It is more reliable than unimodal learning.
Question 2
You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?
A. Decision Trees
B. Linear Regression
C. K-Means Clustering
D. Convolutional Neural Networks (CNN)
Question 3
You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?
A. Bias in data is irrelevant as long as the AI system produces accurate predictions.
B. Bias in data can only be addressed after the AI system has been deployed.
C. The data used to train the AI system may not be representative of the population it is intended to serve.
D. Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
Question 4
What is the correct order of steps in an ML project?
A. Model evaluation, Data collection, Data preprocessing, Model training
B. Model evaluation, Data preprocessing, Model training, Data collection
C. Data preprocessing, Data collection, Model training, Model evaluation
D. Data collection, Data preprocessing, Model training, Model evaluation
Question 5
In LLM evaluation, what does "zero-shot learning" refer to?
A. The model's performance after extensive training
B. The model's ability to learn from zero examples
C. The model's ability to perform tasks it has not been explicitly trained on
D. A technique to reduce training time to zero
Solutions:
| Question 1 Answer: C | Question 2 Answer: D | Question 3 Answer: C | Question 4 Answer: D | Question 5 Answer: C |
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