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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Multimodal model architectures and integration - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data |
| Topic 2: Software Development and Engineering | 15% | - Libraries, frameworks, and tools for multimodal AI - Best practices for building and maintaining systems - Development workflows for generative AI applications |
| Topic 3: 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 |
| Topic 4: Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Topic 5: Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Topic 6: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs |
| Topic 7: 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. Which framework is used for conversational AI models development?
A) NVIDIA Clara
B) NVIDIA Metropolis
C) NVIDIA DeepStream
D) NVIDIA NeMo
2. In experimentation, how does data augmentation contribute to improving model accuracy?
A) It helps in increasing the size of the dataset, leading to better generalization of the model.
B) It improves the interpretability of the model by providing additional insights into the data.
C) It has no impact on model accuracy and is primarily used for data visualization purposes.
D) It reduces the complexity of the model, making it easier to train and evaluate.
3. You want to evaluate the performance of an AI model. Which of the following is a method for AI model evaluation?
A) Interviewing the developers of the AI model to assess its performance.
B) Randomly selecting data points from the training set and calculating the accuracy of the model on these data points.
C) Calculating the loss function of the model on the training set.
D) Calculating the model's accuracy from randomly selected data points from the dataset not used during the model's training.
4. You have been given a dataset with missing values. What is the first step you should take with the data?
A) Fill in the missing values with a default value.
B) Remove the columns with missing values.
C) Remove the rows with missing values.
D) Analyze the patterns and distribution of missing values.
5. What is a main application of Triton Inference Server?
A) Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.
B) Triton Server can be used to deploy neural networks from various frameworks.
C) Triton Server can be used to deploy AI models on the GPU only.
D) Triton Server can be used to generate images from pure noise.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
