NVIDIA NCA-GENM EXAM SIMULATOR ONLINE & NCA-GENM EXAM

NVIDIA NCA-GENM Exam Simulator Online & NCA-GENM Exam

NVIDIA NCA-GENM Exam Simulator Online & NCA-GENM Exam

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Tags: NCA-GENM Exam Simulator Online, NCA-GENM Exam, NCA-GENM Exam Registration, NCA-GENM Top Questions, Test NCA-GENM Dumps.zip

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NVIDIA Generative AI Multimodal Sample Questions (Q88-Q93):

NEW QUESTION # 88
You are developing a multimodal model that combines text and tabular data for predicting customer churn. The text data consists of customer reviews, and the tabular data includes demographics and transaction history. You've preprocessed both datasets. Which of the following approaches would be the MOST effective for integrating these modalities?

  • A. Train separate models for text and tabular data, then average their predictions.
  • B. All of the above.
  • C. Concatenate the raw text and tabular data into a single feature vector.
  • D. Use a Transformer-based model to encode the text and a separate neural network for the tabular data, then fuse the embeddings.
  • E. Convert the text data into numerical features using techniques like TF-IDF, then concatenate these features with the tabular data.

Answer: D,E

Explanation:
Options C and D provides the most effective integration. Using a Transformer-based model for text allows it to capture complex relationships and dependencies in the text. A separate neural network handles tabular data effectively. Fusing the embeddings provides a unified representation. Option D is also valid because it allowst he model to incorporate the text and tabular data together as a single feature vector. Raw concatenation (A) is unlikely to work well. Averaging predictions (B) might not capture interactions between modalities.


NEW QUESTION # 89
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. Use dropout regularization.
  • B. Apply weight decay (L2 regularization).
  • C. Decrease the batch size.
  • D. Increase the size of the training data.
  • E. Increase the learning rate.

Answer: A,B

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 # 90
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

  • A. Reducing the amount of training data
  • B. Adding Ll or L2 regularization.
  • C. Increasing the complexity of the model.
  • D. Using data augmentation techniques.
  • E. Implementing dropout layers.

Answer: B,D,E

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.


NEW QUESTION # 91
You are tasked with evaluating a text-to-video generation model. Which of the following metrics would be MOST appropriate for assessing the temporal coherence and smoothness of the generated videos?

  • A. BLEU score
  • B. Learned Perceptual Image Patch Similarity (LPIPS)
  • C. Frchet Video Distance (FVD)
  • D. Inception Score (IS)
  • E. Frchet Inception Distance (FID)

Answer: C

Explanation:
Frchet Video Distance (FVD) is a metric specifically designed for evaluating video generation models. It extends the concept of FID to the video domain by comparing the distributions of features extracted from real and generated videos, taking into account the temporal dimension. IS and FID are primarily used for image generation. LPIPS measures the perceptual similarity between two images, and BLEU score is used for evaluating text generation.


NEW QUESTION # 92
You are building a multimodal Generative AI model that takes text and images as input to generate a story. The text encoder uses a pre-trained BERT model, and the image encoder uses a pre-trained ResNet50 model. What is the BEST strategy to align the feature spaces of these two encoders during training to ensure effective multimodal fusion?

  • A. Fine-tune only the BERT model while keeping the ResNet50 model frozen.
  • B. Fine-tune only the ResNet50 model while keeping the BERT model frozen.
  • C. Use a contrastive loss function that encourages similar representations for semantically related text and images, and dissimilar representations otherwise. Fine-tune BERT and ResNet50.
  • D. Train a separate linear projection layer for each encoder and minimize the LI distance between the projected features. Freeze BERT and ResNet50.
  • E. Concatenate the outputs of BERT and ResNet50 directly without any alignment strategy.

Answer: C

Explanation:
Contrastive learning is a powerful technique for aligning feature spaces in multimodal learning. By encouraging similar representations for semantically related inputs and dissimilar representations for unrelated inputs, it allows the model to learn a shared representation space that facilitates effective fusion. Fine- tuning both encoders allows for adaptation to the specific task. Other methods are less effective for aligning high-dimensional feature spaces from different modalities.


NEW QUESTION # 93
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