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NVIDIA NCA-GENM Exam Syllabus Topics:

SectionObjectives
Generative AI Concepts- Generative models
  • 1. Transformers and LLM basics
    • 2. Diffusion models
      NVIDIA AI Ecosystem- NVIDIA tools and frameworks
      • 1. NeMo framework usage
        • 2. GPU-accelerated AI workflows
          Multimodal AI Systems- Multimodal model design
          - Cross-modal learning
          • 1. Text-image integration
            • 2. Audio-visual understanding
              Core AI and Machine Learning Fundamentals- Machine learning basics
              • 1. Neural networks fundamentals
                • 2. Supervised and unsupervised learning
                  Responsible and Trustworthy AI- Bias and safety considerations
                  - Ethical AI principles

                  NVIDIA Generative AI Multimodal Sample Questions:

                  1. Which of the following is a disadvantage of the ReLU activation function?

                  A) It is prone to vanishing gradient problem.
                  B) It is computationally expensive.
                  C) It is not suitable for deep neural networks.
                  D) It can cause dead neurons.


                  2. Which of the following is a component of the Content Authenticity Initiative?

                  A) Content credential
                  B) Ethical AI development
                  C) Data encryption
                  D) Content validity


                  3. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds. Which of the following ML models would be the most appropriate choice for this task?

                  A) Linear Regression
                  B) Logistic Regression
                  C) Convolutional Neural Network (CNN)
                  D) K-Means Clustering


                  4. What advantage does multimodal learning have over unimodal learning?

                  A) It is easier to collect multimodal data than unimodal data.
                  B) It is more reliable than unimodal learning.
                  C) It can capture more complex patterns and relationships in data.
                  D) It requires fewer data samples for learning.


                  5. How does the batch size influence VRAM consumption during inference with ML models on GPUs?

                  A) Increasing or decreasing the batch size has the same impact on VRAM consumption.
                  B) Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.
                  C) Decreasing the batch size reduces VRAM consumption.
                  D) The batch size has no impact on VRAM consumption during inference.


                  Solutions:

                  Question # 1
                  Answer: D
                  Question # 2
                  Answer: A
                  Question # 3
                  Answer: C
                  Question # 4
                  Answer: C
                  Question # 5
                  Answer: C

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