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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 |
| Performance Optimization | 10% | - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization |
| 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 |
| Software Development and Engineering | 15% | - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI - Development workflows for generative AI applications |
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Metrics and validation strategies for generative models - Experiment design and methodology |
NVIDIA Generative AI Multimodal Sample Questions:
1. 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 compatibility with different operating systems.
B) The model's processing speed in recognizing faces of different races.
C) The model's ability to recognize various facial expressions.
D) The model's accuracy in recognizing individuals of different races.
2. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?
A) K-Means Clustering
B) Logistic Regression
C) Linear Regression
D) Convolutional Neural Network (CNN)
3. How is the optimization of a multimodal model different from a unimodal model in terms of gradient vanishing?
A) Gradient vanishing is not a concern in either multimodal or unimodal models, as modern optimization techniques have overcome this issue.
B) Unimodal models have a higher risk of gradient vanishing compared to multimodal models, as the focus on a single modality allows for better gradient flow and stability.
C) Both multimodal and unimodal models have an equal risk of gradient vanishing, as the optimization process is independent of the number of modalities.
D) Multimodal models have a higher risk of gradient vanishing compared to unimodal models, as the combination of multiple modalities increases the complexity of the model architecture.
4. You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
A) Bar chart
B) Scatter plot
C) Pie chart
D) Line chart
5. In the transformer architecture, what is the purpose of positional encoding?
A) To add information about the order of each token in the input sequence.
B) To encode the importance of each token in the input sequence.
C) To encode the semantic meaning of each token in the input sequence.
D) To remove redundant information from the input sequence.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: A |
