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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Experimentation | 25% | - Experimental design - Hypothesis testing - Model evaluation and comparison - A/B testing |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
NVIDIA Generative AI Multimodal Sample Questions:
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
- A. Pie chart
- B. Line chart
- C. Bar chart
- D. Scatter plot
Correct Answer: A 🗳️
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. Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
- B. 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.
- C. Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
- D. More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
Correct Answer: A 🗳️
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What is the purpose of the cuDNN library?
- A. To optimize deep neural network computations on NVIDIA GPUs.
- B. To generate images from English text-prompts using CLIP.
- C. To implement GPU-accelerated data preparation and feature extraction.
- D. To measure GPU usage and other metrics with Prometheus.
Correct Answer: A 🗳️
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Which technique is commonly used to speed up AI model training and inference on hardware accelerators?
- A. Dropout
- B. Data augmentation
- C. Model enlargement
- D. Quantization
Correct Answer: D 🗳️
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Which technique involves leveraging pre-trained models to achieve efficient results with less data and computation?
- A. Transfer learning
- B. Neural network integration
- C. State management and composition
- D. Prompt engineering
Correct Answer: A 🗳️
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