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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core Machine Learning and AI Knowledge | 20% | - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning |
| Topic 2: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
| Topic 3: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 4: Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Topic 5: 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 |
| Topic 6: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Topic 7: 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:
1. What does 'kernel fusion' refer to in the context of AI model optimization?
A) Optimizing model inference by reducing the number of computations by pruning.
B) Combining multiple kernels into a single kernel for faster computation.
C) Applying multiple layers of kernels to improve model accuracy.
D) Using kernel functions to optimize model hyperparameters.
2. You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
A) Calculating the model's accuracy from randomly selected data points from the dataset not used during the model's training.
B) Randomly selecting data points from the training set and calculating the accuracy of the model on these data points.
C) Interviewing the developers of the AI model to assess its performance.
D) Calculating the loss function of the model on the training set.
3. In large-language models, what is the purpose of the attention mechanism?
A) To measure the importance of the words in the output sequence.
B) To determine the order in which words are generated.
C) To capture the order of the words in the input sequence.
D) To assign weights to each word in the input sequence.
4. What is a common method to reduce the computational cost of deep learning models during inference?
A) Pruning weights or neurons.
B) Adding more convolutional filters.
C) Increasing the batch size.
D) By replacing activation functions in some neurons with simpler ones.
5. Hyperparameter tuning is used for what purpose in machine learning experimentation?
A) Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.
B) Collecting and preprocessing data to improve the accuracy of the model.
C) Adjusting the weights and biases of a neural network to optimize its performance.
D) Selecting the best ML algorithm for a given task.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: A |
