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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 2: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 3: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 4: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 5: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 6: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 7: Experimentation | 25% | - Experimental design - A/B testing - Hypothesis testing - Model evaluation and comparison |
NVIDIA Generative AI Multimodal Sample Questions:
Question 1
Hyperparameter tuning is used for what purpose in machine learning experimentation?
A. Collecting and preprocessing data to improve the accuracy of the model.
B. Adjusting the weights and biases of a neural network to optimize its performance.
C. Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.
D. Selecting the best ML algorithm for a given task.
Question 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. Linear Regression
B. Convolutional Neural Network (CNN)
C. Logistic Regression
D. K-Means Clustering
Question 3
What role does 'late fusion' play in multimodal machine learning?
A. It refers to the process of combining multiple modalities at the preprocessing stage.
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 decision level.
D. It refers to the process of combining multiple modalities at the feature level.
Question 4
Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?
A. Histogram
B. Pie chart
C. Box plot
D. Heatmap
Question 5
What does 'kernel fusion' refer to in the context of AI model optimization?
A. Applying multiple layers of kernels to improve model accuracy.
B. Combining multiple kernels into a single kernel for faster computation.
C. Using kernel functions to optimize model hyperparameters.
D. Optimizing model inference by reducing the number of computations by pruning.
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: B |
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