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NVIDIA Generative AI Multimodal Sample Questions (Q164-Q169):

NEW QUESTION # 164
You are training a Generative Adversarial Network (GAN) for image synthesis. The discriminator loss is consistently near zero while the generator loss fluctuates significantly. Which of the following is the most likely cause and the best approach to address it?

A. The discriminator is too weak; increase its capacity by adding more layers or filters.
B. Mode collapse is occurring; implement techniques like mini-batch discrimination or spectral normalization.
C. The generator is too weak; reduce its capacity to simplify the learning task.
D. The training data is insufficient; augment the dataset with more diverse images.
E. The learning rate for the discriminator is too high; decrease it substantially.

Answer: B

Explanation:
A discriminator loss near zero indicates it's easily distinguishing real from fake images. The fluctuating generator loss means it's struggling to fool the discriminator. This often signifies mode collapse, where the generator produces a limited variety of outputs. Techniques like mini-batch discrimination (allowing the discriminator to compare the diversity of generated samples) or spectral normalization (constraining the Lipschitz constant of the discriminator) can help prevent this.

NEW QUESTION # 165
You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?

A. Using a simpler model architecture-
B. Implementing hard negative mining.
C. Increasing the embedding dimension-
D. Decreasing the temperature parameter in the contrastive loss.
E. Using a larger batch size during training.

Answer: B

Explanation:
Hard negative mining focuses on selecting the most challenging negative examples (incorrect image-text pairs) during training. This forces the model to learn more robust and discriminative embeddings that generalize better to unseen combinations. Increasing embedding dimension or using larger batch size might help to some extent, but hard negative mining directly addresses the core issue of distinguishing similar but incorrect pairs. Decreasing the temperature parameter can make the contrastive loss too sensitive, potentially hindering generalization. A simpler model architecture may be detrimental if it lacks the capacity to capture the complex relationships

NEW QUESTION # 166
You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text inputs. The pre-trained model was trained on a large dataset of image-caption pairs. Which of the following strategies would be MOST effective for transfer learning in this scenario, considering computational efficiency and performance?

A. Use knowledge distillation to transfer knowledge from the pre-trained model to a smaller, more efficient model.
B. Fine-tune a subset of layers, specifically those responsible for feature extraction from both image and text modalities, while keeping the lower layers frozen.
C. Fine-tune only the classification head (output layer) while freezing all other layers of the pre-trained model.
D. Train a new model from scratch on the new task's dataset.
E. Fine-tune all layers of the pre-trained model with a very small learning rate.

Answer: B

Explanation:
Option C is the most effective strategy. Fine-tuning a subset of layers allows the model to adapt to the new task while leveraging the pre-trained knowledge. Freezing the lower layers preserves the general features learned from the large dataset, while fine-tuning the feature extraction layers allows the model to learn task-specific features. Fine-tuning all layers (Option B) can lead to overfitting and is computationally expensive. Freezing all layers except the classification head (Option A) may not provide sufficient adaptation. Training from scratch (Option D) is computationally expensive and requires a large dataset. Knowledge distillation (Option E) is also a valid option but may not be the most direct approach for transfer learning when the pre-trained model's architecture is suitable.

NEW QUESTION # 167
Consider the following PyTorch code snippet for a multimodal loss function:

What is the MOST significant issue with this code, preventing it from working as intended for a multimodal task?

A. The function only works for a specific batch size.
B. The code lacks normalization of image and text features before computing the loss.
C. The code uses 'CrossEntropyLosS , which is not suitable for feature vectors but for classification scores.
D. The code doesn't include any regularization to prevent overfitting.
E. The 'alpha' parameter is not being used correctly to balance the image and text losses.

Answer: C

Explanation:
The 'CrossEntropyLoss' expects classification scores as input (logits before softmax), not feature vectors. The code passes the feature vectors directly to the loss function, which will lead to incorrect and meaningless results. The other options are less critical: the alpha parameter is used correctly even if the balancing might be suboptimal, feature normalization and regularization are always beneficial but not strictly required, and the function does not require a specific batch size.

NEW QUESTION # 168
Consider the following Python code snippet, which attempts to implement a basic form of cross-validation. What is the primary issue with this code and how would you fix it to prevent data leakage?

A. Data leakage occurs because the 'KFold" split is not shuffled. Fix: Set in the "KFold' constructor.
B. Data leakage occurs because the model is being reinitialized in each fold. Fix: Move the model initialization outside the loop.
C. Data leakage occurs because feature scaling is applied to the entire dataset before splitting it into training and testing sets. Fix: Apply scaling separately to the training and testing sets within each fold.
D. The code is correct and doesn't have any data leakage issues.
E. Data leakage occurs because the model is not being evaluated on a hold-out set Fix: Create a separate validation set and evaluate the model on it after each fold.

Answer: C

Explanation:
Data leakage happens because the StandardScaler is fit on the entire dataset before splitting into training and testing sets. This means that the scaling parameters (mean and standard deviation) are influenced by the test data, which then leaks information into the training process, leading to overoptimistic performance estimates. The correct fix is to fit the scaler only on the training data within each fold and then use the fitted scaler to transform both the training and testing data.

NEW QUESTION # 169
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