NCA-GENM NEW BRAINDUMPS FILES | ACCURATE NVIDIA GENERATIVE AI MULTIMODAL 100% FREE VALID EXAM NOTES

NCA-GENM New Braindumps Files | Accurate NVIDIA Generative AI Multimodal 100% Free Valid Exam Notes

NCA-GENM New Braindumps Files | Accurate NVIDIA Generative AI Multimodal 100% Free Valid Exam Notes

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Tags: NCA-GENM New Braindumps Files, NCA-GENM Valid Exam Notes, Certified NCA-GENM Questions, Simulated NCA-GENM Test, Latest NCA-GENM Real Test

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The NVIDIA NCA-GENM mock tests are specially built for you to evaluate what you have studied. These NVIDIA Generative AI Multimodal (NCA-GENM) practice exams (desktop and web-based) are customizable, which means that you can change the time and questions according to your needs. Our NVIDIA Generative AI Multimodal (NCA-GENM) practice tests teach you time management so you can pass the NVIDIA Generative AI Multimodal (NCA-GENM) certification exam.

NVIDIA Generative AI Multimodal Sample Questions (Q71-Q76):

NEW QUESTION # 71
You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model's generalization ability? (Select TWO)

  • A. Decreasing the amount of training data.
  • B. Increasing the learning rate.
  • C. Decreasing batch size.
  • D. Using weight decay (L2 regularization).
  • E. Applying data augmentation techniques (e.g., random synonym replacement, back-translation).

Answer: D,E

Explanation:
Weight decay penalizes large weights, preventing the model from overfitting to the training data. Data augmentation increases the diversity of the training data, making the model more robust to variations in unseen data. Increasing the learning rate can lead to instability and overfitting. Decreasing the amount of training data reduces the model's ability to learn general patterns.


NEW QUESTION # 72
You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)

  • A. Increase the size of the training data.
  • B. Increase the learning rate.
  • C. Decrease the batch size.
  • D. Use dropout regularization.
  • E. Apply weight decay (L2 regularization).

Answer: D,E

Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to new data. Weight decay (L2 regularization) penalizes large weights, preventing the model from becoming too complex. Dropout randomly deactivates neurons during training, forcing the model to learn more robust features. Increasing the learning rate might worsen overfitting. Decreasing the batch size can sometimes act as a regularizer, but its primary effect is on the training dynamics. While more training data is generally beneficial, if the new data is very similar to the existing training data, it won't necessarily solve the overfitting issue.


NEW QUESTION # 73
You are developing a multimodal generative A1 model that takes both image and text inputs. The image branch uses a ResNet50 pre- trained on ImageNet, while the text branch uses a BERT model. To effectively combine the features, you need to align their representations. Which of the following techniques is MOST suitable for projecting the image and text features into a common embedding space?

  • A. Employing Contrastive Learning with a shared embedding space and using positive and negative pairs of image and text.
  • B. Fine-tuning the entire ResNet50 and BERT models jointly on the multimodal dataset.
  • C. Direct concatenation of ResNet50 and BERT output features.
  • D. Using Principal Component Analysis (PCA) to reduce the dimensionality of ResNet50 and BERT features before concatenation.
  • E. Training separate linear projection layers for both ResNet50 and BERT outputs, followed by concatenation.

Answer: A

Explanation:
Contrastive learning is highly effective for aligning representations from different modalities. By training the model to pull together embeddings of related image-text pairs while pushing apart embeddings of unrelated pairs, it learns a shared embedding space where semantically similar concepts are close to each other, regardless of their modality. While (B) is a possible approach, it doesn't explicitly enforce alignment based on semantic similarity. (A) is unlikely to produce good results due to differing feature spaces. (C) is computationally expensive. (D) is a dimensionality reduction technique, not primarily an alignment method.


NEW QUESTION # 74
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?

  • A. Cross-entropy loss
  • B. Kullback-Leibler (KL) divergence loss
  • C. Hinge loss
  • D. Perceptual loss
  • E. Contrastive loss

Answer: B

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 # 75
You are building a real-time image captioning system using a Transformer model. You observe significant latency issues when generating captions for high-resolution images. Which optimization strategies would be most effective in reducing the latency without significantly sacrificing caption quality? (Select all that apply)

  • A. Increase the number of Transformer layers to improve caption accuracy, even if it increases latency.
  • B. Implement knowledge distillation, training a smaller, faster student model to mimic the behavior of the larger Transformer model.
  • C. Employ model quantization techniques (e.g., INT8 quantization) to reduce model size and memory bandwidth requirements.
  • D. Use mixed precision training and inference, leveraging Tensor Cores on NVIDIA GPUs.
  • E. Decrease the batch size during inference.

Answer: B,C,D

Explanation:
Quantization reduces model size and increases inference speed, knowledge distillation creates a smaller, faster model and mixed precision leverages Tensor Cores for faster computation. Decreasing the batch size (A) can reduce latency for individual requests, but it may not be the most efficient way to utilize hardware resources. Increasing Transformer layers (D) would increase latency.


NEW QUESTION # 76
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