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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 2: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 3: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 4: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 5: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 6: Experimentation | 25% | - Experimental design - A/B testing - Hypothesis testing - Model evaluation and comparison |
| Topic 7: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
NVIDIA Generative AI Multimodal Sample Questions:
1. Which of the following best describes the role of the Hugging Face model repository in ML software development?
A) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
B) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
C) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
D) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
2. In experimentation, how does data augmentation contribute to improving model accuracy?
A) It helps in increasing the size of the dataset, leading to better generalization of the model.
B) It improves the interpretability of the model by providing additional insights into the data.
C) It has no impact on model accuracy and is primarily used for data visualization purposes.
D) It reduces the complexity of the model, making it easier to train and evaluate.
3. You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A) Line chart
B) Bar chart
C) Pie chart
D) Scatter plot
4. Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
A) Dropout
B) Quantization
C) Data augmentation
D) Model enlargement
5. What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?
A) To generate new images from pure noise.
B) To classify input images as noisy or clean.
C) To segment noisy patches in input images.
D) To detect noisy objects in input images.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A |
