Quiz 2026 NVIDIA Fantastic Pass4sure NCA-GENM Exam Prep

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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                  NVIDIA Generative AI Multimodal Sample Questions (Q33-Q38):

                  NEW QUESTION # 33
                  You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?

                  Answer: D

                  Explanation:
                  Modality dropout forces the model to learn robust representations from each modality independently, making it less reliant on the presence of both modalities. This improves performance when one modality is missing or of low quality. Training only on high-quality images (A) would exacerbate the problem. Reducing the image modality's weight (C) might help slightly but doesn't fundamentally address the issue. Using a simpler image model (D) would likely decrease overall performance. Increasing the training dataset size with only high-quality images (E) will not address the problem of the model's dependence on high-quality images.


                  NEW QUESTION # 34
                  You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?

                  Answer: C

                  Explanation:
                  Hard negative mining focuses on selecting the most challenging negative examples (incorrect image-text pairs) during training. This forces the model to learn more robust and discriminative embeddings that generalize better to unseen combinations. Increasing embedding dimension or using larger batch size might help to some extent, but hard negative mining directly addresses the core issue of distinguishing similar but incorrect pairs. Decreasing the temperature parameter can make the contrastive loss too sensitive, potentially hindering generalization. A simpler model architecture may be detrimental if it lacks the capacity to capture the complex relationships


                  NEW QUESTION # 35
                  You are building a multimodal model that takes images and text descriptions as input to generate new images. You want to evaluate the impact of different image encoders (ResNet50, Efficient Net) on the generated image quality and relevance to the text prompt. Which evaluation metric(s) would be MOST appropriate for this task?

                  Answer: D

                  Explanation:
                  FID measures the distance between the feature distributions of generated and real images, indicating image quality and diversity. CLIP Score measures the similarity between the generated image and the text prompt, evaluating relevance. IS more suitable for evaluating unimodal image generation. Perplexity and BLEU score are for text generation.


                  NEW QUESTION # 36
                  You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?

                  Answer: D

                  Explanation:
                  The Kullback-Leibler (KL) divergence loss is a crucial component of the VAE objective function. It measures the difference between the learned latent space distribution and a prior distribution (typically a standard Gaussian). Adding the KL divergence loss encourages the latent space to be well-structured and continuous, which helps generate sharper and more realistic images. The other loss functions serve different purposes and are not typically used in VAEs for this specific reason. Cross-entropy is for classification. Perceptual loss helps in transferring styles. Contrastive loss used to learn embedding. Hinge loss mostly used in SVM.


                  NEW QUESTION # 37
                  You are fine-tuning a pre-trained Generative A1 model for a specific downstream task with limited dat a. The model starts to exhibit catastrophic forgetting of the original pre-trained knowledge. Which technique could mitigate this issue effectively?

                  Answer: B,D


                  NEW QUESTION # 38
                  ......

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