NCA-GENM PDF EXAM DUMP - TEST NCA-GENM PRACTICE

NCA-GENM Pdf Exam Dump - Test NCA-GENM Practice

NCA-GENM Pdf Exam Dump - Test NCA-GENM Practice

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NVIDIA Generative AI Multimodal Sample Questions (Q144-Q149):

NEW QUESTION # 144
You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?

  • A. t-SNE (t-distributed Stochastic Neighbor Embedding)
  • B. Attention Heatmaps
  • C. Confusion Matrix
  • D. ROC curves (Receiver Operating Characteristic curves)
  • E. PCA (Principal Component Analysis)

Answer: B

Explanation:
Attention heatmaps are a visualization technique used to highlight the regions of an image that the model is focusing on when processing specific words in the input text. By overlaying the attention weights onto the image, you can identify the most relevant areas. t-SNE and PCA are dimensionality reduction techniques used for visualizing high-dimensional data in lower dimensions. ROC curves and confusion matrices are used to evaluate the performance of classification models.


NEW QUESTION # 145
You are tasked with analyzing a large dataset of images used for training a generative A1 model. The dataset contains noisy labels and varying image quality. Which of the following preprocessing steps are MOST crucial for improving the performance of your model?

  • A. Resizing all images to a fixed resolution (e.g., 256x256).
  • B. Implementing a label smoothing technique to mitigate the impact of noisy labels.
  • C. Using a pre-trained image quality assessment model to filter out low-quality images.
  • D. Applying aggressive data augmentation techniques like random rotations and flips.
  • E. Converting all images to grayscale to reduce computational complexity.

Answer: B,C

Explanation:
Label smoothing addresses noisy labels by preventing the model from becoming overconfident in incorrect labels. Filtering low- quality images improves the overall data quality and prevents the model from learning from corrupted data. While resizing and data augmentation can be beneficial, they are less critical than handling noisy labels and poor image quality in this scenario. Converting to grayscale might reduce computation, but could remove crucial color information for the generative model.


NEW QUESTION # 146
You're building a multimodal model to generate captions for videos. You've noticed that your model struggles to capture temporal relationships and sequential dependencies in the video frames. Which of the following architectures or techniques would be BEST suited to address this?

  • A. A 3D Convolutional Neural Network (3D CNN) that processes multiple frames as a volume.
  • B. Principal Component Analysis (PCA) to reduce dimensionality of each frame before feeding to the decoder
  • C. A Multilayer Perceptron (MLP) trained on flattened video frames.
  • D. A standard Convolutional Neural Network (CNN) applied independently to each frame.
  • E. A combination of a CNN to extract features from individual frames, followed by a Recurrent Neural Network (RNN) like LSTM or GRU to model temporal dependencies between the extracted features.

Answer: E

Explanation:
RNNs, specifically LSTMs and GRUs, are designed to handle sequential data and capture temporal relationships. Combining a CNN for feature extraction with an RNN allows the model to process individual frames and then model the dependencies between them. 3D CNNs can also capture temporal information, but can be computationally expensive. Other options don't explicitly address the temporal aspect.


NEW QUESTION # 147
You are building a generative model that takes both image and text input to generate novel images. You are using a Variational Autoencoder (VAE) architecture with separate encoders for images and text. After training, you observe that the generated images are heavily influenced by the image input and barely incorporate the text information. Which of the following techniques would MOST likely improve the incorporation of text information into the generated images?

  • A. Removing the text encoder and only using the image encoder.
  • B. Decreasing the capacity of the text encoder.
  • C. Train two separate VAE models. One for Text and another for images.
  • D. Increasing the capacity of the image encoder and decoder.
  • E. Using a cross-attention mechanism in the decoder to allow the image features to attend to the text features during image generatiom

Answer: E

Explanation:
A cross-attention mechanism allows the image features to selectively attend to the relevant parts of the text features during the image generation process. This enables the model to effectively incorporate the text information into the generated images- Increasing the capacity of the image encoder/decoder might further bias the model towards the image input Decreasing the capacity of the text encoder would further reduce the influence of text. Removing the text encoder is obviously not a solution- Training two separate VAE models won't generate correlated Image and Text.


NEW QUESTION # 148
Which of the following techniques are MOST effective for improving the energy efficiency of a large-scale Generative A1 model during inference, while minimizing performance degradation?

  • A. Model quantization (e.g., INT8)
  • B. Gradient accumulation
  • C. Pruning (removing less important weights)
  • D. Increasing the batch size significantly
  • E. Knowledge distillation to a smaller model

Answer: A,C,E

Explanation:
Model quantization reduces the memory footprint and computational cost by representing weights with fewer bits. Knowledge distillation trains a smaller, faster model to mimic the behavior of a larger model. Pruning removes redundant connections, reducing the number of computations. Gradient accumulation is for training, not inference. Increasing batch size may improve throughput but not necessarily energy efficiency per sample and might even decrease it due to increased memory usage.


NEW QUESTION # 149
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