PASS GUARANTEED QUIZ HIGH PASS-RATE NCA-GENM - LATEST NVIDIA GENERATIVE AI MULTIMODAL EXAM REGISTRATION

Pass Guaranteed Quiz High Pass-Rate NCA-GENM - Latest NVIDIA Generative AI Multimodal Exam Registration

Pass Guaranteed Quiz High Pass-Rate NCA-GENM - Latest NVIDIA Generative AI Multimodal Exam Registration

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NVIDIA Generative AI Multimodal Sample Questions (Q258-Q263):

NEW QUESTION # 258
Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?

  • A. Spearman's rank correlation coefficient
  • B. Cohen's Kappa
  • C. Chi-squared test
  • D. Krippendorff's Alpha
  • E. Pearson correlation coefficient

Answer: D

Explanation:
Krippendorff's Alpha is the most appropriate measure when dealing with multiple annotators, ordinal data (severity scores on a scale), and potential for missing data. While Cohen's Kappa is suitable for two annotators, Krippendorff's Alpha generalizes to multiple annotators. Spearman's rank correlation is better suited for assessing the monotonic relationship between variables, but not agreement among raters. Pearson correlation requires interval data, while chi-squared is for categorical.


NEW QUESTION # 259
You're designing a U-Net architecture for generating high-resolution medical images from low-resolution scans. Which of the following considerations are MOST crucial for maintaining fine-grained detail during the upsampling process, and how might NVIDIA's NeMo framework assist?

  • A. Employing a very deep network architecture to capture complex relationships between pixels. NeMo aids in managing the complexity and training of such deep networks with optimized optimizers and distributed training capabilities.
  • B. Ignoring the low resolution features and concentrate on better latent space sampling. NeMo can provide models to enhance sampling techniques.
  • C. Incorporating skip connections from the contracting path to the expanding path, allowing the network to leverage high-resolution features from earlier layers. NeMo provides modules for efficient skip connection implementation and management of feature map sizes.
  • D. Using only bilinear interpolation in the upsampling layers to avoid introducing artifacts. NeMo can assist by providing pre-trained interpolation layers.
  • E. Using only transpose convolutional layers for upsampling to learn the optimal upsampling filters. NeMo offers optimized transpose convolution implementations for performance.

Answer: C

Explanation:
Skip connections are essential in U-Nets for preserving fine-grained detail. They allow the network to access high-resolution features learned in the contracting path during the upsampling process. NeMo's features for managing skip connections and feature map sizes can streamline the implementation. While transpose convolutions (D) can be useful, they are not the most crucial without skip connections. Bilinear interpolation alone is generally insufficient for high-resolution image generation. NeMo can aid with (C) but it's not as crucial as skip connections. (E) is incorrect because it is crucial to leverage information extracted during the downsampling process.


NEW QUESTION # 260
You are building a multimodal generative A1 application that uses CLIP to align text and image embeddings. You observe that the generated images lack detail and fidelity to the text prompt. Which of the following strategies would be MOST effective in improving image quality, and how could prompt engineering and Triton Inference Server play a role?

  • A. Refining the text prompts to be more descriptive and specific, incorporating stylistic details and relevant keywords. Triton can optimize the prompt embedding process.
  • B. All of the above
  • C. Training a separate image super-resolution model to enhance the generated images after they are produced by the CLIP-guided generator. Triton can manage the concurrent execution of the generator and super-resolution models.
  • D. Using a larger batch size during CLIP training and increasing the learning rate. Triton is not directly involved in model training.
  • E. Increasing the CLIP model's text encoder's hidden layer size and using more aggressive data augmentation during CLIP training. Triton can be used to serve the augmented CLIP model at scale.

Answer: A,C

Explanation:
Refining text prompts (B) with prompt engineering is crucial for guiding the generative process toward desired outputs. A more specific prompt provides better guidance for the image generator. Using a separate super-resolution model (C) addresses the detail issue directly. While increasing CLIP's capacity (A) could help, it's less direct than prompt engineering or super-resolution and may be computationally expensive. Larger batch sizes and learning rates (D) are training parameters and do not directly address the image quality problem after training. Triton's role in serving various models (CLIP, generator, super-resolution) concurrently is key to a seamless pipeline. Therefore both B and C are the most effective strategies.


NEW QUESTION # 261
Consider the following code snippet using a hypothetical Generative A1 library. This code is intended to generate an image from a text prompt and then refine it based on a user-provided style image. However, it's not producing the desired results. What is the MOST likely cause of the issue?

  • A. The 'strength' parameter in 'refine_image' is set too low, resulting in minimal stylistic changes.
  • B. The 'generate_image' function does not support the parameter.
  • C. The 'style_image' is not preprocessed correctly before being passed to the 'refine_image' function.
  • D. The library being used is incompatible with the GPU.
  • E. The text prompt provided is too short.

Answer: A

Explanation:
A low 'strength' value will result in the 'refine_image' function making only very subtle changes based on the 'style_image' While other factors could contribute, this is the most direct and likely cause of the observed behavior. The question doesn't present any issues related to preprocessing or library errors that would impact the other choices.


NEW QUESTION # 262
You are analyzing the latent space of a GAN trained to generate images of human faces. You notice that interpolating between two points in the latent space often results in unrealistic or distorted faces. Which of the following techniques could potentially improve the smoothness and interpretability of the latent space?

  • A. Using spectral normalization in the discriminator network.
  • B. Applying a regularization term to the latent space during training to encourage smoothness (e.g., a Laplacian prior).
  • C. Increasing the number of layers in the discriminator network.
  • D. Using a smaller batch size during training.
  • E. Decreasing the learning rate of the generator network.

Answer: B

Explanation:
Regularizing the latent space directly encourages smoothness, making interpolations more realistic. Spectral normalization in the discriminator improves training stability but doesn't directly address latent space smoothness. Increasing discriminator layers or decreasing generator learning rate might influence performance, but regularization is the most direct approach. Batch size is less impactful on latent space interpretability.


NEW QUESTION # 263
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