- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
- Updated: Sep 03, 2026
- Q & A: 58 Questions and Answers
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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Associate - Generative AI Multimodal |
| Exam Number: | NCA-GENM |
| Exam Price: | $125 USD |
| Available Languages: | English |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple-choice |
| Real Exam Qty: | 50-60 |
| Related Certifications: | NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO) NVIDIA Certified Associate - Generative AI LLMs (NCA-GENL) |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | 2 years |
| Sample Questions: | ![]() |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | A basic understanding of generative AI and multimodal models. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal/ |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 2: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 3: Experimentation | 25% | - Model evaluation and comparison - Hypothesis testing - A/B testing - Experimental design |
| Topic 4: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 5: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 6: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 7: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
NVIDIA Generative AI Multimodal is an official certification exam from NVIDIA, identified by the exam code NCA-GENM. Pass it and you hold the NVIDIA-Certified Associate certification, which sits at the Associate level. It also feeds into related credentials, including NVIDIA Certified Associate - Generative AI LLMs (NCA-GENL), NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO). In practical terms, this certification tells employers your skills have been measured against the vendor's own standard, which is why certified candidates tend to stand out in hiring and promotion decisions.
Expect 50-60 questions within 60 minutes on the NVIDIA Generative AI Multimodal exam. Numbers aside, the real challenge is rhythm: lingering too long on one item quietly steals time from three others. Build the habit of answering, flagging, and moving on, then rehearse the full experience under a countdown with the Actual4Dumps test engine until finishing comfortably early becomes your norm.
Registration for NVIDIA Generative AI Multimodal officially runs $125 USD, with Not publicly disclosed required to pass. Worth knowing before you book: retakes are billed at the same full $125 USD, so a failed attempt doubles your spend. The smarter sequence is to measure yourself first. When your scores on the Actual4Dumps practice tests clear the passing line with room to spare, consistently, that is your signal to schedule the real thing.
A basic understanding of generative AI and multimodal models.
Because vendors update their policies without much fanfare, verify the latest entry requirements before you book using the official exam page.
Yes, and we encourage it. Download the free PDF demo of the NVIDIA Generative AI Multimodal questions from Actual4Dumps and evaluate the quality on your own terms. After you buy, every update for the next 365 days is free, and when that window closes you can extend your update service at half price.
A 100% money-back guarantee applies, governed by clear conditions. If you sit the NVIDIA Generative AI Multimodal exam within 60 days of purchase and fail, you can claim a full refund, as long as the exam corresponds to your product. Attempts made within 3 days of purchase are ineligible, as are products you downloaded but never used, free materials, and expired orders; the name on the exam record must match the payer's name. File your claim with a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and it is settled within 7 days. Rather have a replacement than a refund? You may exchange for two other exam products of equal value, free, and your original product keeps its update service.
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The NVIDIA Generative AI Multimodal blueprint spans 7 domains. The ones carrying the most weight include Trustworthy AI (5%), Multimodal Data (15%), and Data Analysis & Visualization (10%). The complete domain-by-domain breakdown is in the exam topics section above; treat it as your master checklist and let the weights tell you where your study hours earn the most.
Question 1
What does 'kernel fusion' refer to in the context of AI model optimization?
A. Using kernel functions to optimize model hyperparameters.
B. Combining multiple kernels into a single kernel for faster computation.
C. Applying multiple layers of kernels to improve model accuracy.
D. Optimizing model inference by reducing the number of computations by pruning.
Question 2
In a Generative Adversarial Network (GAN), what is the role of the discriminator?
A. To optimize the training process.
B. To generate new data based on the training set.
C. To calculate the loss function and update the generator.
D. To distinguish between real and generated data.
Question 3
In the transformer architecture, what is the purpose of positional encoding?
A. To add information about the order of each token in the input sequence.
B. To remove redundant information from the input sequence.
C. To encode the importance of each token in the input sequence.
D. To encode the semantic meaning of each token in the input sequence.
Question 4
In multimodal machine learning, what does 'early fusion' refer to?
A. Integrating different modalities at the beginning of the model pipeline.
B. Implementing the model in the early stages of development of the ML solution.
C. Training separate models for each modality and then combining their predictions.
D. Ignoring certain modalities and only using one modality for analysis and prediction.
Question 5
What advantage does multimodal learning have over unimodal learning?
A. It can capture more complex patterns and relationships in data.
B. It requires fewer data samples for learning.
C. It is more reliable than unimodal learning.
D. It is easier to collect multimodal data than unimodal data.
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: A | Question 5 Answer: A |
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