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NVIDIA NCA-GENM Q&A - in .pdf
- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
- Updated: Nov 08, 2026
- Q & A: 58 Questions and Answers
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- Exam Code: NCA-GENM
- Exam Name: NVIDIA Generative AI Multimodal
- Updated: Nov 08, 2026
- Q & A: 58 Questions and Answers
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 2: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 3: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 4: Experimentation | 25% | - A/B testing - Model evaluation and comparison - Hypothesis testing - Experimental design |
| Topic 5: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 6: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 7: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
NVIDIA Generative AI Multimodal Sample Questions:
Question #1
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 and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
- B. 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.
- C. 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.
- D. 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.
- 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.
Correct Answer: C,D 🗳️
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Question #2
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 can only be addressed after the AI system has been deployed.
- B. The data used to train the AI system may not be representative of the population it is intended to serve.
- C. Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
- D. Bias in data is irrelevant as long as the AI system produces accurate predictions.
Correct Answer: B 🗳️
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Question #3
What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
- A. CLIP is used to generate image captions from textual input.
- B. CLIP provides a common embedding space for both the textual and image modalities.
- C. CLIP is used to convert textual input into image embeddings.
- D. CLIP is used to enhance datasets through data augmentation for text-to-image generation.
Correct Answer: B 🗳️
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Question #4
In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?
- A. It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
- B. It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
- C. It requires AI systems to be developed with an ethical consideration for societal impacts.
- D. It ensures that AI systems are transparent in their decision-making processes.
Correct Answer: A 🗳️
Question #5
What are some methods to overcome limited throughput between CPU and GPU?
- A. Using techniques like memory pooling.
- B. Increase the number of CPU cores.
- C. Increase the clock speed of the CPU.
- D. Upgrade the GPU to a higher-end model.
Correct Answer: A 🗳️
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