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, PrepPDF 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.

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  • Exam Code: NCA-GENM
  • Exam Name: NVIDIA Generative AI Multimodal
  • Certification Provider: NVIDIA
  • Corresponding Certification: NVIDIA-Certified Associate
  • Updated: Sep 18, 2026
  • No. of Questions: 58 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

                  NVIDIA Generative AI Multimodal Sample Questions:

                  What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.

                  • A. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.
                  • 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. 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.
                  • D. 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.
                  • E. 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.
                  Reveal Solution  Discussion  0

                  Correct Answer: C,E  🗳️

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

                  In a multimodal machine learning context, how are different modalities usually linked to each other?

                  • A. Different modalities are linked through a shared representation that captures the relationships between the modalities.
                  • B. Different modalities are linked through random connections.
                  • C. Different modalities are linked through separate models that are ensembled by tree-based models.
                  • D. Different modalities are not linked to each other in a multimodal machine learning context.
                  Reveal Solution  Discussion  0

                  Correct Answer: A  🗳️

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                  Which framework is used for conversational AI models development?

                  • A. NVIDIA DeepStream
                  • B. NVIDIA Clara
                  • C. NVIDIA NeMo
                  • D. NVIDIA Metropolis
                  Reveal Solution  Discussion  0

                  Correct Answer: C  🗳️

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

                  You are developing a GenAI-Multimodal system that uses data from various sources. What is one potential issue you need to consider in relation to bias in data?

                  • A. Bias in data is not a concern for AI systems as they are designed to be neutral and objective.
                  • B. The data used to train the AI system may not be representative of the population it is intended to serve.
                  • C. Bias in data can only be addressed after the AI system has been deployed.
                  • D. Bias in data is irrelevant as long as the AI system produces accurate predictions.
                  Reveal Solution  Discussion  0

                  Correct Answer: B  🗳️

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

                  What is a main application of Triton Inference Server?

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

                  Correct Answer: D  🗳️

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

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