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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Visualization | 10% | - Visualization techniques for model behavior and results - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs |
| Topic 2: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Characteristics of text, image, and audio data - Multimodal model architectures and integration |
| Topic 3: Performance Optimization | 10% | - Model efficiency and inference optimization - Hardware acceleration with NVIDIA platforms - Scalability and deployment considerations |
| Topic 4: Trustworthy AI | 5% | - Robustness and error mitigation - Ethical considerations and responsible use - Reliability, fairness, and safety in generative systems |
| Topic 5: 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 |
| Topic 6: Experimentation | 25% | - Model training, fine-tuning, and evaluation - Experiment design and methodology - Metrics and validation strategies for generative models |
| Topic 7: 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 |
NVIDIA Generative AI Multimodal Sample Questions:
1. What is a main application of Triton Inference Server?
A) Triton Server can be used to deploy AI models on the GPU only.
B) Triton Server can be used to deploy neural networks from various frameworks.
C) Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.
D) Triton Server can be used to generate images from pure noise.
2. You are tasked with developing an image processing model using machine learning. You need to classify thousands of labeled images of cats and dogs. Which algorithm is commonly used for image classification?
A) Convolutional Neural Networks (CNN)
B) Linear Regression
C) Decision Trees
D) K-Means Clustering
3. What are some methods to overcome limited throughput between CPU and GPU?
A) Increase the number of CPU cores.
B) Using techniques like memory pooling.
C) Upgrade the GPU to a higher-end model.
D) Increase the clock speed of the CPU.
4. In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?
A) K-means clustering
B) Support vector machine (SVM)
C) Generative adversarial network (GAN)
D) Decision tree
5. Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?
A) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
B) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
C) Training a deep learning model using the images in the dataset to find outliers and enhancing the quality of MRI scans using image processing techniques.
D) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: D |
