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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Experimentation | 25% | - Metrics and validation strategies for generative models - Model training, fine-tuning, and evaluation - Experiment design and methodology |
| Topic 2: Core Machine Learning and AI Knowledge | 20% | - Fundamental concepts of machine learning and deep learning - Generative AI principles and techniques - Neural network architectures relevant to multimodal systems |
| Topic 3: Trustworthy AI | 5% | - Ethical considerations and responsible use - Robustness and error mitigation - Reliability, fairness, and safety in generative systems |
| Topic 4: Performance Optimization | 10% | - Hardware acceleration with NVIDIA platforms - Model efficiency and inference optimization - Scalability and deployment considerations |
| Topic 5: Multimodal Data | 15% | - Data preprocessing, fusion, and representation - Multimodal model architectures and integration - Characteristics of text, image, and audio data |
| Topic 6: 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 |
| Topic 7: Data Analysis and Visualization | 10% | - Interpretation of generative AI outputs - Analyzing multimodal datasets and outputs - Visualization techniques for model behavior and results |
NVIDIA Generative AI Multimodal Sample Questions:
In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?
- A. Decision tree
- B. Support vector machine (SVM)
- C. K-means clustering
- D. Generative adversarial network (GAN)
Correct Answer: D 🗳️
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In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?
- A. It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
- B. It requires AI systems to be developed with an ethical consideration for societal impacts.
- C. It ensures that AI systems are transparent in their decision-making processes.
- D. It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
Correct Answer: D 🗳️
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What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.
- A. In a multimodal context, usually, contrastive learning decreases the similarity of representations across modalities for the same objects and increases the similarity of representations across modalities for different objects.
- B. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.
- C. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.
- D. Contrastive learning is a technique used to train deep learning models by comparing similar and dissimilar inputs and optimizing the model to maximize the similarity between representations of similar inputs and minimize the similarity between representations of dissimilar inputs.
- E. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
Correct Answer: C,D 🗳️
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Which of the following best describes the purpose of GAN (Generative Adversarial Networks)?
- A. To optimize search algorithms for faster data retrieval.
- B. To produce new data that is similar to the training data.
- C. To classify and categorize data based on patterns and features.
- D. To optimize decision-making processes based on historical data.
Correct Answer: B 🗳️
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Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?
- A. CTC Loss
- B. Word Error Rate (WER)
- C. F1 Score
- D. Mean Opinion Score (MOS)
Correct Answer: B 🗳️
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