NVIDIA NCA-GENM Questions & Answers - in .pdf
- Total Q&A: 58
- Update: Aug 28, 2026
- Price: $59.99
- Vendor: NVIDIA
- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
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NVIDIA NCA-GENM Q&A - Testing Engine
- Total Q&A: 58
- Update: Aug 28, 2026
- Price: $59.99
- 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: | NVIDIA Generative AI Multimodal Certification Exam (NCA-GENM) |
| Exam Number: | NCA-GENM |
| Related Certifications: | NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL) |
| Real Exam Qty: | 50-60 |
| Exam Price: | $125 USD |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Available Languages: | English |
| Recommended Training: | NVIDIA Deep Learning Institute (DLI) Courses |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCA-GENM Sample Questions |
| 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 |
|---|---|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Generative AI Concepts | - Generative models
|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
What role does 'late fusion' play in multimodal machine learning?
A. It refers to the process of combining multiple modalities at the preprocessing stage.
B. It refers to the process of combining multiple modalities at the feature level.
C. It refers to the process of combining multiple modalities at the training stage.
D. It refers to the process of combining multiple modalities at the decision level.
Question 2
In convolutional neural networks, we may use padding in both convolution and transposed convolution.
Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.
A. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
B. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
C. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
D. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.
E. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.
Question 3
What does 'modality alignment' refer to?
A. Aligning different modalities within multimodal data to ensure meaningful connections and associations.
B. The integration of pretrained models to perform custom tasks involving different types of data.
C. The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
D. Addressing challenges related to missing or incomplete information across different modalities.
Question 4
Which of the following is a disadvantage of the ReLU activation function?
A. It is computationally expensive.
B. It can cause dead neurons.
C. It is prone to vanishing gradient problem.
D. It is not suitable for deep neural networks.
Question 5
What is the purpose of the cuDNN library?
A. To measure GPU usage and other metrics with Prometheus.
B. To implement GPU-accelerated data preparation and feature extraction.
C. To generate images from English text-prompts using CLIP.
D. To optimize deep neural network computations on NVIDIA GPUs.
Solutions:
| Question 1 Answer: D | Question 2 Answer: A,B | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: D |
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