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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Experimentation | 25% | - Experimental design - Hypothesis testing - Model evaluation and comparison - A/B testing |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| 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 |
NVIDIA Generative AI Multimodal Sample Questions:
1. Which of the following best describes the role of the Hugging Face model repository in ML software development?
A) A set of NVIDIA SDKs, such as Riva, NeMo, Triton, and ACE, for implementing neural network architectures.
B) A convenient tool for deploying neural networks for production-scale inference similar to Triton Server.
C) A library for customizing large language models like GPT, LLaMA-2, and Falcon using the NeMo framework.
D) A platform for sharing and accessing pre-trained models and transformers for natural language processing.
2. During the process of data cleansing, which of the following steps is NOT typically performed?
A) Transforming data into a different format
B) Removing duplicates
C) Collecting additional data
D) Identifying and handling missing values
3. What is a common method to reduce the computational cost of deep learning models during inference?
A) Adding more convolutional filters.
B) By replacing activation functions in some neurons with simpler ones.
C) Increasing the batch size.
D) Pruning weights or neurons.
4. What does 'modality alignment' refer to?
A) Addressing challenges related to missing or incomplete information across different modalities.
B) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
C) Aligning different modalities within multimodal data to ensure meaningful connections and associations.
D) The integration of pretrained models to perform custom tasks involving different types of data.
5. How does CLIP understand the content of both text and images?
A) By translating images into text and comparing them with the prompt.
B) By converting text and images into a frequency domain for comparison.
C) Using contrastive learning to match images with text descriptions.
D) Through a database of predefined images with their descriptions.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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