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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 2: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 3: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 4: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 5: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 6: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 7: Experimentation | 25% | - Experimental design - Hypothesis testing - Model evaluation and comparison - A/B testing |
NVIDIA Generative AI Multimodal Sample Questions:
1. What is the purpose of the cuDNN library?
A) To implement GPU-accelerated data preparation and feature extraction.
B) To generate images from English text-prompts using CLIP.
C) To optimize deep neural network computations on NVIDIA GPUs.
D) To measure GPU usage and other metrics with Prometheus.
2. What are some methods to overcome limited throughput between CPU and GPU?
A) Using techniques like memory pooling.
B) Increase the clock speed of the CPU.
C) Increase the number of CPU cores.
D) Upgrade the GPU to a higher-end model.
3. Which of the following best describes the role of machine learning in handling multimodal data?
A) To reduce the amount of data needed for accurate predictions.
B) To focus on textual data analysis.
C) To enable models to learn from and interpret diverse data types.
D) To eliminate the need for human intervention in data analysis.
4. 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) Directly combining MRI scans and radiology reports into a single input stream without preprocessing or modality-specific adjustments.
B) Implementing separate unimodal pipelines for each modality to ensure the data is informative and the model design is accurate.
C) More advanced natural language processing techniques to interpret radiology reports, ignoring the MRI scans' diagnostic value.
D) 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.
5. Which metric is commonly used to evaluate machine-translation models?
A) F1 score
B) Accuracy
C) Mean Absolute Error (MAE)
D) BLEU score
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: D |
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