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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations - Model efficiency and inference optimization |
| Multimodal Data | 15% | - Multimodal model architectures and integration - Characteristics of text, image, and audio data - Data preprocessing, fusion, and representation |
| 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 - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Experimentation | 25% | - Metrics and validation strategies for generative models - Experiment design and methodology - Model training, fine-tuning, and evaluation |
| Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Neural network architectures relevant to multimodal systems - Generative AI principles and techniques |
| 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 |
NVIDIA Generative AI Multimodal Sample Questions:
1. In the transformer architecture, what is the purpose of positional encoding?
A) To remove redundant information from the input sequence.
B) To encode the importance of each token in the input sequence.
C) To add information about the order of each token in the input sequence.
D) To encode the semantic meaning of each token in the input sequence.
2. What does mixed-precision training refer to?
A) Training a model using multiple precision levels, such as using both single-precision and double- precision floating-point numbers.
B) Training a model using different types of data, such as text, images, audio, time series, and geospatial information.
C) Training a model using incomplete or missing information from different modalities.
D) Training a model using diverse data types while addressing challenges related to missing or incomplete information.
3. What is the significance of A/B testing in ML software engineering?
A) A/B testing is used to measure the impact of changes in the user interface of a ML application.
B) A/B testing is irrelevant in ML software engineering.
C) A/B testing helps in evaluating the performance and effectiveness of different machine learning models.
D) A/B testing helps in optimizing the hyperparameters of a machine learning model.
4. What are some methods to overcome limited throughput between CPU and GPU?
A) Upgrade the GPU to a higher-end model.
B) Increase the clock speed of the CPU.
C) Using techniques like memory pooling.
D) Increase the number of CPU cores.
5. You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?
A) The data used to train the AI system may not be representative of the population it is intended to serve.
B) Bias in data is irrelevant as long as the AI system produces accurate predictions.
C) Bias in data can only be addressed after the AI system has been deployed.
D) Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: A |




