Exam Name: NVIDIA Generative AI Multimodal
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Associate
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Instant Download NVIDIA : NCA-GENM Questions & Answers as PDF & Test Engine
Updated: Oct 23, 2026
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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Topic 2: Generative AI Concepts | - Generative models
|
| Topic 3: NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Topic 4: Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Topic 5: Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
NVIDIA Generative AI Multimodal Sample Questions:
Which framework is used for conversational AI models development?
- A. NVIDIA DeepStream
- B. NVIDIA NeMo
- C. NVIDIA Clara
- D. NVIDIA Metropolis
Correct Answer: B 🗳️
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What is a common method to reduce the computational cost of deep learning models during inference?
- A. Adding more convolutional filters.
- B. By replacing activation functions in some neurons with simpler ones.
- C. Pruning weights or neurons.
- D. Increasing the batch size.
Correct Answer: C 🗳️
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What does mixed-precision training refer to?
- A. Training a model using multiple precision levels, such as using both single-precision and double- precision floating-point numbers.
- B. Training a model using diverse data types while addressing challenges related to missing or incomplete information.
- C. Training a model using incomplete or missing information from different modalities.
- D. Training a model using different types of data, such as text, images, audio, time series, and geospatial information.
Correct Answer: A 🗳️
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How is the optimization of a multimodal model different from a unimodal model in terms of gradient vanishing?
- A. Unimodal models have a higher risk of gradient vanishing compared to multimodal models, as the focus on a single modality allows for better gradient flow and stability.
- B. Both multimodal and unimodal models have an equal risk of gradient vanishing, as the optimization process is independent of the number of modalities.
- C. Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.
- D. Multimodal models have a higher risk of gradient vanishing compared to unimodal models, as the combination of multiple modalities increases the complexity of the model architecture.
Correct Answer: D 🗳️
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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 decision 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 feature level.
Correct Answer: B 🗳️
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