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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Topic 2: Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Topic 3: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 4: Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
| Topic 5: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Topic 6: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| Topic 7: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
NVIDIA Generative AI Multimodal Sample Questions:
Question 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 platform for sharing and accessing pre-trained models and transformers for natural language processing.
C. A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
D. A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
Question 2
Which of the following tasks can be performed using the transformer LLM encoder model?
A. Image generation
B. Speech recognition
C. Generating code
D. Semantic analysis
Question 3
What is a common method to reduce the computational cost of deep learning models during inference?
A. Pruning weights or neurons.
B. Increasing the batch size.
C. Adding more convolutional filters.
D. By replacing activation functions in some neurons with simpler ones.
Question 4
What does 'modality alignment' refer to?
A. Aligning different modalities within multimodal data to ensure meaningful connections and associations.
B. The integration of pretrained models to perform custom tasks involving different types of data.
C. Addressing challenges related to missing or incomplete information across different modalities.
D. The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
Question 5
In LLM evaluation, what does "zero-shot learning" refer to?
A. The model's ability to learn from zero examples
B. The model's ability to perform tasks it has not been explicitly trained on
C. A technique to reduce training time to zero
D. The model's performance after extensive training
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: A | Question 5 Answer: B |
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