NVIDIA Generative AI Multimodal: NCA-GENM Exam


"NVIDIA Generative AI Multimodal", also known as NCA-GENM exam, is a NVIDIA Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 58 Q&As to your NCA-GENM Exam preparation. In the NCA-GENM exam resources, you will cover every field and category in NVIDIA-Certified Associate Certification helping to ready you for your successful NVIDIA Certification.

  • Exam Code: NCA-GENM
  • Exam Name: NVIDIA Generative AI Multimodal
  • Total Questions: 58

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

                  NVIDIA Generative AI Multimodal Sample Questions:

                  What characteristic of autoencoders makes them suitable for anomaly detection?

                  • A. Their capacity to learn a compressed representation of the data.
                  • B. Their capability to predict future outcomes based on past data.
                  • C. Their ability to classify images with high accuracy.
                  • D. Their function in enhancing the quality of images.
                  Reveal Solution  Discussion  0

                  Correct Answer: A  🗳️

                  Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).

                  In convolutional neural networks, we may use padding in both convolution and transposed convolution.
                  Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.

                  • A. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
                  • B. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.
                  • C. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
                  • D. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
                  • E. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.
                  Reveal Solution  Discussion  0

                  Correct Answer: C,D  🗳️

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                  What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?

                  • A. To classify input images as noisy or clean.
                  • B. To segment noisy patches in input images.
                  • C. To detect noisy objects in input images.
                  • D. To generate new images from pure noise.
                  Reveal Solution  Discussion  0

                  Correct Answer: D  🗳️

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                  What is a main application of Triton Inference Server?

                  • A. Triton Server can be used to generate images from pure noise.
                  • B. Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.
                  • C. Triton Server can be used to deploy neural networks from various frameworks.
                  • D. Triton Server can be used to deploy AI models on the GPU only.
                  Reveal Solution  Discussion  0

                  Correct Answer: C  🗳️

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                  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 generate images from English text-prompts using CLIP.
                  • D. To measure GPU usage and other metrics with Prometheus.
                  Reveal Solution  Discussion  0

                  Correct Answer: A  🗳️

                  Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).

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