NVIDIA NCA-GENM dumps - in .pdf

NCA-GENM pdf
  • Exam Code: NCA-GENM
  • Exam Name: NVIDIA Generative AI Multimodal
  • Updated: Sep 16, 2026
  • Q & A: 58 Questions and Answers
  • PDF Price: $59.99
  • Free Demo

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  • Exam Code: NCA-GENM
  • Exam Name: NVIDIA Generative AI Multimodal
  • Updated: Sep 16, 2026
  • Q & A: 58 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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NVIDIA NCA-GENM dumps - Testing Engine

NCA-GENM Testing Engine
  • Exam Code: NCA-GENM
  • Exam Name: NVIDIA Generative AI Multimodal
  • Updated: Sep 16, 2026
  • Q & A: 58 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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NVIDIA NCA-GENM Exam Syllabus Topics:

SectionObjectives
Topic 1: Generative AI Concepts- Generative models
  • 1. Diffusion models
    • 2. Transformers and LLM basics
      Topic 2: Responsible and Trustworthy AI- Bias and safety considerations
      - Ethical AI principles
      Topic 3: Core AI and Machine Learning Fundamentals- Machine learning basics
      • 1. Supervised and unsupervised learning
        • 2. Neural networks fundamentals
          Topic 4: NVIDIA AI Ecosystem- NVIDIA tools and frameworks
          • 1. GPU-accelerated AI workflows
            • 2. NeMo framework usage
              Topic 5: Multimodal AI Systems- Multimodal model design
              - Cross-modal learning
              • 1. Text-image integration
                • 2. Audio-visual understanding

                  NVIDIA Generative AI Multimodal Sample Questions:

                  Question #1

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

                  • A. It is computationally expensive.
                  • B. It can cause dead neurons.
                  • C. It is prone to vanishing gradient problem.
                  • D. It is not suitable for deep neural networks.
                  Reveal Solution  Discussion  0

                  Correct Answer: B  🗳️

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

                  Question #2

                  What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?

                  • A. CLIP is used to enhance datasets through data augmentation for text-to-image generation.
                  • B. CLIP is used to convert textual input into image embeddings.
                  • C. CLIP is used to generate image captions from textual input.
                  • D. CLIP provides a common embedding space for both the textual and image modalities.
                  Reveal Solution  Discussion  0

                  Correct Answer: D  🗳️

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

                  Question #3

                  In a Generative Adversarial Network (GAN), what is the role of the discriminator?

                  • A. To generate new data based on the training set.
                  • B. To distinguish between real and generated data.
                  • C. To calculate the loss function and update the generator.
                  • D. To optimize the training process.
                  Reveal Solution  Discussion  0

                  Correct Answer: B  🗳️

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

                  Question #4

                  What is the correct order of steps in an ML project?

                  • A. Data preprocessing, Data collection, Model training, Model evaluation
                  • B. Model evaluation, Data collection, Data preprocessing, Model training
                  • C. Data collection, Data preprocessing, Model training, Model evaluation
                  • D. Model evaluation, Data preprocessing, Model training, Data collection
                  Reveal Solution  Discussion  0

                  Correct Answer: C  🗳️

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

                  Question #5

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

                  • A. The batch size has no impact on VRAM consumption during inference.
                  • B. Increasing or decreasing the batch size has the same impact on VRAM consumption.
                  • C. Decreasing the batch size reduces VRAM consumption.
                  • D. Increasing the batch size reduces VRAM consumption because more data can be processed in parallel.
                  Reveal Solution  Discussion  0

                  Correct Answer: C  🗳️

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

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