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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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                  NCA-GENM Test Dates & Relevant NCA-GENM Answers

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                  NVIDIA Generative AI Multimodal Sample Questions (Q14-Q19):

                  NEW QUESTION # 14
                  You are working on a Generative A1 Multimodal model that takes text and audio as input and generates a video. During training, you observe that the generated videos often lack coherence with the input text. What are the potential issues you would investigate? (Select THREE)

                  Answer: A,C,E

                  Explanation:
                  Insufficient regularization can cause overfitting and lack of generalization, leading to incoherence. A weak conditioning mechanism means the model isn't effectively using the input text to guide the video generation. A lack of diverse training examples limits the model's ability to learn the relationships between text, audio, and video. A too-powerful discriminator can lead to mode collapse, but primarily affects diversity, not necessarily coherence directly. Input audio loudness is a preprocessing issue, not a fundamental architectural problem.


                  NEW QUESTION # 15
                  Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?

                  Answer: A

                  Explanation:
                  Increasing the 'guidance_scales forces the generated image to adhere more closely to the prompt. Adding a negative prompt helps remove unwanted artifacts and improves image quality. Negative prompt is a well know prompt engginerring technique to remove unwanted objects or attributes.


                  NEW QUESTION # 16
                  You are tasked with generating realistic images of human faces using a GAN. However, you notice that the generated images often contain artifacts, such as distorted facial features or unrealistic textures. Which of the following techniques would be most effective in improving the realism and quality of the generated faces?

                  Answer: B

                  Explanation:
                  StyleGAN architecture, with its AdalN and mapping network, is specifically designed to control and manipulate the style attributes of generated images, leading to more realistic and high-quality outputs, particularly for complex structures like human faces. AdalN helps in normalizing feature statistics based on style codes, enabling fine-grained control over the visual appearance.


                  NEW QUESTION # 17
                  Which of the following techniques are MOST likely to improve the energy efficiency of a large-scale multimodal AI model without significantly sacrificing accuracy?

                  Answer: A,B,C

                  Explanation:
                  Model quantization reduces the memory footprint and computational requirements by using lower precision numbers. Knowledge distillation transfers knowledge from a large model to a smaller one, reducing the computational cost. Pruning removes redundant connections, making the model more efficient. Increasing batch size (Option B) can improve throughput but doesn't inherently reduce energy consumption per sample. Using a larger model (Option D) increases energy consumption.


                  NEW QUESTION # 18
                  You are tasked with optimizing a Generative A1 model that processes both image and text dat a. The current model uses a simple concatenation of image features (extracted from a ResNet-50) and text embeddings (from BERT) as input to a transformer. You observe that the model struggles to generate coherent descriptions for complex images. Which of the following optimization strategies would be MOST effective in improving the model's understanding of the multimodal input?

                  Answer: C

                  Explanation:
                  Cross-attention allows the model to learn which parts of the image are most relevant to each word in the text, enabling a more nuanced understanding of the relationship between the two modalities. Concatenation treats all features equally, which is less effective. Increasing transformer size or ResNet architecture might help but doesn't address the core issue of multimodal interaction.


                  NEW QUESTION # 19
                  ......

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