ACTUAL NCA-GENM TEST PDF, REAL NCA-GENM TORRENT

Actual NCA-GENM Test Pdf, Real NCA-GENM Torrent

Actual NCA-GENM Test Pdf, Real NCA-GENM Torrent

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NVIDIA Generative AI Multimodal Sample Questions (Q301-Q306):

NEW QUESTION # 301
You are working on a multimodal model for video captioning, where the model needs to generate captions describing the actions and events happening in a video. You notice that the model tends to focus only on the most salient objects in the scene and ignores subtle but important actions. Which of the following techniques can help the model attend to these subtle actions and generate more comprehensive captions?

  • A. Implementing a hierarchical attention mechanism that first attends to relevant time steps and then to relevant regions within those time steps.
  • B. Decreasing the regularization strength.
  • C. Using a larger batch size.
  • D. Increasing the learning rate during training.
  • E. Adding more layers to the LSTM or GRIJ used for sequence modeling.

Answer: A

Explanation:
A hierarchical attention mechanism is the MOST appropriate technique. By first attending to relevant time steps (which might contain the subtle actions) and then attending to relevant regions within those time steps, the model can focus on the specific parts of the video that are most informative for describing the subtle actions. Increasing learning rate (A), using a larger batch size (B), adding more layers (D), and decreasing regularization strength (E) are unlikely to solve the problem of attending to subtle actions specifically. These can all improve performance, but they don't address the attention mechanism itself.


NEW QUESTION # 302
You are developing a system to summarize patient medical records, which include doctor's notes (text), lab results (time-series data), and X-ray images. Which of the following techniques would be MOST effective in integrating these diverse data types to generate a coherent and comprehensive summary?

  • A. Convert all data types to text using OCR and other techniques, then train a single text summarization model.
  • B. Use a multimodal transformer model that can process text, time-series, and image data as input, creating a joint representation for summarization.
  • C. Train separate summarization models for each data type and concatenate the resulting summaries.
  • D. Use separate summarization models, weigh them by their perceived information content and average.
  • E. Ignore the lab results and X-ray images and focus only on summarizing the doctor's notes.

Answer: B

Explanation:
A multimodal transformer model is designed to handle different data types as input and learn relationships between them, generating a coherent and comprehensive summary. Other options may lead to loss of information or a disjointed summary.


NEW QUESTION # 303
You are tasked with building a system that generates realistic images based on both textual descriptions and a semantic segmentation map. The segmentation map provides spatial information about the objects present in the scene. Which of the following generative architectures is MOST appropriate for this multimodal task?

  • A. Vanilla Generative Adversarial Network (GAN)
  • B. Variational Autoencoder (VAE)
  • C. Conditional Generative Adversarial Network (cGAN) with both text and segmentation map as conditions.
  • D. Diffusion model without conditioning
  • E. Autoregressive model like PixelCNN

Answer: C

Explanation:
A Conditional GAN (cGAN) is the MOST suitable architecture. cGANs allow you to condition the image generation process on additional information, such as text and segmentation maps. By providing both modalities as conditions, the generator can learn to create images that are consistent with both the textual description and the spatial layout defined by the segmentation map. Vanilla GANs and VAEs don't offer explicit conditioning mechanisms. Autoregressive models can generate high-quality images, but they don't easily accommodate multimodal inputs like text and segmentation maps. A diffusion model without conditioning does not have the capacity to generate images from multimodal prompts.


NEW QUESTION # 304
In the context of generative models, what is the primary purpose of using a normalizing flow?

  • A. To regularize the training of the discriminator in a GAN.
  • B. To transform a simple probability distribution (e.g., Gaussian) into a more complex and flexible distribution that can better model the data.
  • C. To improve the computational efficiency of backpropagation.
  • D. To reduce the dimensionality of the input data before feeding it to a generator network.
  • E. To introduce non-linearity into the encoder network of a VAE

Answer: B

Explanation:
Normalizing flows are used to transform a simple probability distribution into a complex one by applying a series of invertible transformations. This allows the model to learn more complex data distributions and generate more realistic samples.


NEW QUESTION # 305
You have a multimodal model combining video and text data for action recognition. The model performs well on standard datasets but struggles with videos containing unusual camera angles or lighting conditions. Which data augmentation strategy would be MOST effective in improving the model's robustness?

  • A. Adding random noise to the audio track.
  • B. Applying random rotations, flips, and color jittering to the video frames.
  • C. Randomly cropping and scaling the video frames.
  • D. Replacing random words in the text descriptions with synonyms.
  • E. Reducing the frame rate of the videos.

Answer: B

Explanation:
Applying random rotations, flips, and color jittering directly addresses the model's sensitivity to camera angles and lighting conditions by exposing it to a wider range of visual variations during training. The other options are less relevant to these specific issues. Audio noise is irrelevant to visual robustness. Cropping and scaling are basic augmentations but less effective than transformations that simulate camera angles and lighting. Synonym replacement improves text understanding, but not visual robustness. Reducing frame rate can reduce computation but doesn't improve robustness to visual variations.


NEW QUESTION # 306
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