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NVIDIA NCA-GENM exam : NVIDIA Generative AI Multimodal

NCA-GENM Exam Simulator
  • Exam Code: NCA-GENM
  • Exam Name: NVIDIA Generative AI Multimodal
  • Updated: Nov 06, 2026
  • Q & A: 58 Questions and Answers
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NVIDIA NCA-GENM Exam Syllabus Topics:

SectionWeightObjectives
Core Machine Learning and AI Knowledge20%- Generative AI principles and techniques
- Neural network architectures relevant to multimodal systems
- Fundamental concepts of machine learning and deep learning
Performance Optimization10%- Model efficiency and inference optimization
- Scalability and deployment considerations
- Hardware acceleration with NVIDIA platforms
Trustworthy AI5%- Reliability, fairness, and safety in generative systems
- Ethical considerations and responsible use
- Robustness and error mitigation
Data Analysis and Visualization10%- Interpretation of generative AI outputs
- Visualization techniques for model behavior and results
- Analyzing multimodal datasets and outputs
Experimentation25%- Experiment design and methodology
- Metrics and validation strategies for generative models
- Model training, fine-tuning, and evaluation
Multimodal Data15%- Characteristics of text, image, and audio data
- Multimodal model architectures and integration
- Data preprocessing, fusion, and representation
Software Development and Engineering15%- Best practices for building and maintaining systems
- Libraries, frameworks, and tools for multimodal AI
- Development workflows for generative AI applications

NVIDIA Generative AI Multimodal Sample Questions:

Question #1

Which of the following best describes the role of machine learning in handling multimodal data?

  • A. To focus on textual data analysis.
  • B. To enable models to learn from and interpret diverse data types.
  • C. To eliminate the need for human intervention in data analysis.
  • D. To reduce the amount of data needed for accurate predictions.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

What is a main application of Triton Inference Server?

  • A. Triton Server can be used to deploy AI models on the GPU only.
  • B. Triton Server can be used to generate images from pure noise.
  • C. Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.
  • D. Triton Server can be used to deploy neural networks from various frameworks.
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Correct Answer: D  🗳️

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Question #3

How does the batch size influence VRAM consumption during inference with ML models on GPUs?

  • A. Decreasing the batch size reduces VRAM consumption.
  • B. Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.
  • C. The batch size has no impact on VRAM consumption during inference.
  • D. Increasing or decreasing the batch size has the same impact on VRAM consumption.
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Correct Answer: A  🗳️

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Question #4

In LLM evaluation, what does "zero-shot learning" refer to?

  • A. The model's ability to learn from zero examples
  • B. The model's performance after extensive training
  • C. The model's ability to perform tasks it has not been explicitly trained on
  • D. A technique to reduce training time to zero
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Correct Answer: C  🗳️

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Question #5

Which technique is commonly used to speed up AI model training and inference on hardware accelerators?

  • A. Data augmentation
  • B. Model enlargement
  • C. Dropout
  • D. Quantization
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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