Exam Name: NVIDIA Generative AI Multimodal
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Associate
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Updated: Sep 03, 2026
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NVIDIA NCA-GENM Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Generative AI Multimodal Certification Exam (NCA-GENM) |
| Exam Number: | NCA-GENM |
| Related Certifications: | NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL) |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Real Exam Qty: | 50-60 |
| Exam Price: | $125 USD |
| Recommended Training: | NVIDIA Deep Learning Institute (DLI) Courses |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam (remote) |
| Pre Condition: | Basic understanding of generative AI and machine learning concepts is recommended; familiarity with Python and neural networks is helpful. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-multimodal-associate/ |
NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Multimodal AI Systems | - Cross-modal learning
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Generative AI Concepts | - Generative models
|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
FAQs for NVIDIA Generative AI Multimodal Candidates
The NCA-GENM exam leads to the NVIDIA Certified Associate: Generative AI Multimodal certification from NVIDIA. It is positioned at the Associate level. The exam validates the knowledge and skills expected of candidates preparing for NVIDIA Generative AI Multimodal.
Related certifications include:
- NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL)
The NCA-GENM exam includes 50-60 questions and allows 60 minutes. Before test day, divide the available time by the number of questions in a timed practice set to establish a steady per-question pace. If a difficult item starts consuming too much time, mark it for review and move forward instead of losing momentum. A full timed practice session can help you decide when to move on, when to return, and how much buffer to reserve for a final check.
Basic understanding of generative AI and machine learning concepts is recommended; familiarity with Python and neural networks is helpful.
Eligibility rules can change, so confirm the current requirements on the official exam page before registering.
You can register through the official channels listed below:
The available exam delivery method is Online proctored exam (remote).
The official training options include:
After reviewing the recommended training, reinforce what you learned with Actual4dump's 58 practice questions for NCA-GENM.
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If you purchase the corresponding NCA-GENM preparation material and do not pass that exam within 60 days of purchase, you may apply for a full refund under the 100% Money Back Guarantee. The request requires a scanned exam enrollment slip and the official Score Report PDF, submitted within two days after the exam. Eligible requests are processed within seven days.
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The current NCA-GENM outline is organized into 5 top-level domains. The first listed areas include:
- Core AI and Machine Learning Fundamentals
- Generative AI Concepts
- Responsible and Trustworthy AI
Review the Exam Topics section above for the complete outline and any nested subtopics.
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A. Line chart
B. Pie chart
C. Bar chart
D. Scatter plot
Question 2
How does CLIP understand the content of both text and images?
A. Through a database of predefined images with their descriptions.
B. By converting text and images into a frequency domain for comparison.
C. Using contrastive learning to match images with text descriptions.
D. By translating images into text and comparing them with the prompt.
Question 3
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
A. The model's compatibility with different operating systems.
B. The model's ability to recognize various facial expressions.
C. The model's accuracy in recognizing individuals of different races.
D. The model's processing speed in recognizing faces of different races.
Question 4
In the context of multimodal machine learning, what does 'data fusion' refer to?
A. Combining different modalities of data into a single representation.
B. Removing missing or incomplete information from different modalities.
C. Evaluating the quality of diverse data types in multimodal machine learning.
D. Separating different modalities of data into distinct representations.
Question 5
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A. Line chart
B. Pie chart
C. Bar chart
D. Scatter plot
Solutions:
| Question 1 Answer: B | Question 2 Answer: C | Question 3 Answer: C | Question 4 Answer: A | Question 5 Answer: B |
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