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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems - Robustness and error mitigation |
| Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| 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 |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Core Machine Learning and AI Knowledge | 20% | - Generative AI principles and techniques - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems |
| Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Visualization techniques for model behavior and results - Analyzing multimodal datasets and outputs |
NVIDIA Generative AI Multimodal Sample Questions:
Question #1
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 rows with missing values.
C. Remove the columns with missing values.
D. Analyze the patterns and distribution of missing values.
Question #2
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 assign weights to each word in the input sequence.
C. To determine the order in which words are generated.
D. To capture the order of the words in the input sequence.
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 ability to recognize various facial expressions.
B. The model's processing speed in recognizing faces of different races.
C. The model's accuracy in recognizing individuals of different races.
D. The model's compatibility with different operating systems.
Question #4
Which of the following is a component of the Content Authenticity Initiative?
A. Ethical AI development
B. Data encryption
C. Content validity
D. Content credential
Question #5
Which of the following is a disadvantage of the ReLU activation function?
A. It is computationally expensive.
B. It is not suitable for deep neural networks.
C. It can cause dead neurons.
D. It is prone to vanishing gradient problem.
Solutions:
| Question #1 Correct Answer: D | Question #2 Correct Answer: B | Question #3 Correct Answer: C | Question #4 Correct Answer: D | Question #5 Correct Answer: C |
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