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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Robustness and error mitigation - Ethical considerations and responsible use |
| 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 |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Experimentation | 25% | - Experiment design and methodology - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models |
| Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques |
| Multimodal Data | 15% | - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation - Multimodal model architectures and integration |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
A. CLIP is used to generate image captions from textual input.
B. CLIP provides a common embedding space for both the textual and image modalities.
C. CLIP is used to enhance datasets through data augmentation for text-to-image generation.
D. CLIP is used to convert textual input into image embeddings.
Question 2
What role does 'late fusion' play in multimodal machine learning?
A. It refers to the process of combining multiple modalities at the feature level.
B. It refers to the process of combining multiple modalities at the training stage.
C. It refers to the process of combining multiple modalities at the preprocessing stage.
D. It refers to the process of combining multiple modalities at the decision level.
Question 3
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
A. The model's processing speed in recognizing faces of different races.
B. The model's accuracy in recognizing individuals of different races.
C. The model's compatibility with different operating systems.
D. The model's ability to recognize various facial expressions.
Question 4
In LLM evaluation, what does "zero-shot learning" refer to?
A. A technique to reduce training time to zero
B. The model's ability to learn from zero examples
C. The model's performance after extensive training
D. The model's ability to perform tasks it has not been explicitly trained on
Question 5
What does 'kernel fusion' refer to in the context of AI model optimization?
A. Combining multiple kernels into a single kernel for faster computation.
B. Applying multiple layers of kernels to improve model accuracy.
C. Optimizing model inference by reducing the number of computations by pruning.
D. Using kernel functions to optimize model hyperparameters.
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: A |
