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NVIDIA NCA-GENM Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Experimentation | 25% | - A/B testing - Hypothesis testing - Experimental design - Model evaluation and comparison |
| Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
NVIDIA Generative AI Multimodal Sample Questions:
1. Which metric is commonly used to evaluate machine-translation models?
A) BLEU score
B) Mean Absolute Error (MAE)
C) F1 score
D) Accuracy
2. In a Generative Adversarial Network (GAN), what is the role of the discriminator?
A) To distinguish between real and generated data.
B) To calculate the loss function and update the generator.
C) To optimize the training process.
D) To generate new data based on the training set.
3. What is the purpose of the cuDNN library?
A) To optimize deep neural network computations on NVIDIA GPUs.
B) To implement GPU-accelerated data preparation and feature extraction.
C) To measure GPU usage and other metrics with Prometheus.
D) To generate images from English text-prompts using CLIP.
4. You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
A) The model's accuracy in recognizing individuals of different races.
B) The model's compatibility with different operating systems.
C) The model's processing speed in recognizing faces of different races.
D) The model's ability to recognize various facial expressions.
5. In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?
A) It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.
B) It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.
C) It requires AI systems to be developed with an ethical consideration for societal impacts.
D) It ensures that AI systems are transparent in their decision-making processes.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: A |
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