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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Experimentation | 25% | - Experimental design - Model evaluation and comparison - Hypothesis testing - A/B testing |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
NVIDIA Generative AI Multimodal Sample Questions:
1. What does 'kernel fusion' refer to in the context of AI model optimization?
A) Optimizing model inference by reducing the number of computations by pruning.
B) Using kernel functions to optimize model hyperparameters.
C) Applying multiple layers of kernels to improve model accuracy.
D) Combining multiple kernels into a single kernel for faster computation.
2. In the context of multimodal machine learning, what does 'data fusion' refer to?
A) Removing missing or incomplete information from different modalities.
B) Evaluating the quality of diverse data types in multimodal machine learning.
C) Separating different modalities of data into distinct representations.
D) Combining different modalities of data into a single representation.
3. What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?
A) To segment noisy patches in input images.
B) To detect noisy objects in input images.
C) To generate new images from pure noise.
D) To classify input images as noisy or clean.
4. In the transformer architecture, what is the purpose of positional encoding?
A) To encode the semantic meaning of each token in the input sequence.
B) To remove redundant information from the input sequence.
C) To encode the importance of each token in the input sequence.
D) To add information about the order of each token in the input sequence.
5. In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?
A) It requires AI systems to be developed with an ethical consideration for societal impacts.
B) It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
C) It ensures that AI systems are transparent in their decision-making processes.
D) It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: D |
