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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Multimodal model architectures and integration - Data preprocessing, fusion, and representation |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Trustworthy AI | 5% | - Robustness and error mitigation - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Development workflows for generative AI applications - Libraries, frameworks, and tools for multimodal AI |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| 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. Which of the following best describes the role of the Hugging Face model repository in ML software development?
A) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
B) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
C) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
D) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
2. What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.
A) Contrastive learning is a technique used to train deep learning models by comparing similar and dissimilar inputs and optimizing the model to maximize the similarity between representations of similar inputs and minimize the similarity between representations of dissimilar inputs.
B) In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.
C) Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
D) In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.
E) In a multimodal context, usually, contrastive learning decreases the similarity of representations across modalities for the same objects and increases the similarity of representations across modalities for different objects.
3. In a multimodal machine learning context, how are different modalities usually linked to each other?
A) Different modalities are linked through separate models that are ensembled by tree-based models.
B) Different modalities are linked through a shared representation that captures the relationships between the modalities.
C) Different modalities are linked through random connections.
D) Different modalities are not linked to each other in a multimodal machine learning context.
4. In experimentation, how does data augmentation contribute to improving model accuracy?
A) It reduces the complexity of the model, making it easier to train and evaluate.
B) It has no impact on model accuracy and is primarily used for data visualization purposes.
C) It helps in increasing the size of the dataset, leading to better generalization of the model.
D) It improves the interpretability of the model by providing additional insights into the data.
5. Which of the following tasks can be performed using the transformer LLM encoder model?
A) Speech recognition
B) Semantic analysis
C) Generating code
D) Image generation
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A,D | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: B |
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