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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
| Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Experimentation | 25% | - Experiment design and methodology - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems |
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning |
NVIDIA Generative AI Multimodal Sample Questions:
1. You have been given a dataset with missing values. What is the first step you should take with the data?
A) Fill in the missing values with a default value.
B) Remove the columns with missing values.
C) Analyze the patterns and distribution of missing values.
D) Remove the rows with missing values.
2. What are some methods to overcome limited throughput between CPU and GPU?
A) Using techniques like memory pooling.
B) Upgrade the GPU to a higher-end model.
C) Increase the number of CPU cores.
D) Increase the clock speed of the CPU.
3. You are conducting an experiment to evaluate the performance of different AI models. What is the purpose of AI model evaluation?
A) To determine the best AI model architecture.
B) To determine the ethical implications of AI model usage.
C) To study the impact of AI models on human behavior.
D) To analyze the cost-effectiveness of AI model development.
4. Which of the following best describes the role of the Hugging Face model repository in ML software development?
A) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
B) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
C) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
D) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
5. In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?
A) It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
B) It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
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.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
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