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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Trustworthy AI | 5% | - Reliability, fairness, and safety in generative systems - Ethical considerations and responsible use - Robustness and error mitigation |
| Software Development and Engineering | 15% | - Development workflows for generative AI applications - Best practices for building and maintaining systems - Libraries, frameworks, and tools for multimodal AI |
| 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 |
| Performance Optimization | 10% | - Model efficiency and inference optimization - Scalability and deployment considerations - Hardware acceleration with NVIDIA platforms |
| Data Analysis and Visualization | 10% | - Analyzing multimodal datasets and outputs - Interpretation of generative AI outputs - Visualization techniques for model behavior and results |
| Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
NVIDIA Generative AI Multimodal Sample Questions:
1. In LLM evaluation, what does "zero-shot learning" refer to?
A) The model's performance after extensive training
B) A technique to reduce training time to zero
C) The model's ability to perform tasks it has not been explicitly trained on
D) The model's ability to learn from zero examples
2. What is the purpose of a kernel in a Convolutional Neural Network (CNN)?
A) To normalize the input data.
B) To calculate the loss function.
C) To perform convolution operations on input data.
D) To classify the data into different categories.
3. Which of the following is a disadvantage of the ReLU activation function?
A) It is computationally expensive.
B) It can cause dead neurons.
C) It is not suitable for deep neural networks.
D) It is prone to vanishing gradient problem.
4. During the process of data cleansing, which of the following steps is NOT typically performed?
A) Removing duplicates
B) Transforming data into a different format
C) Identifying and handling missing values
D) Collecting additional data
5. In large-language models, what is the purpose of the attention mechanism?
A) To capture the order of the words in the input sequence.
B) To determine the order in which words are generated.
C) To measure the importance of the words in the output sequence.
D) To assign weights to each word in the input sequence.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: D |
Emmanuel 
Hogan 
Dominic 
Morton 
Iris 
Nicola 
Cedric 
Jeffrey 
Harvey 
Felix 
Ingram 
Felix 
Madge 
Michell 
Laurel 
Lilith 
Rachel 
Michael 
Mark 
Ira 
Asa 
Doreen 
Pete 
Payne 
