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NVIDIA Generative AI Multimodal Sample Questions (Q23-Q28):

NEW QUESTION # 23
Assume you have trained a text-to-image diffusion model using a large dataset of landscape photographs. You now want to adapt this model to generate images of photorealistic portraits. Which of the following fine-tuning strategies is most likely to yield the best results with the least amount of training data and time?

A. Fine-tune the IJ-Net architecture of the diffusion model while keeping the CLIP model fixed.
B. Fine-tune only the CLIP model with portrait-related text descriptions and corresponding images.
C. Fine-tune both the CLIP model and the U-Net architecture with the portrait dataset, using a smaller learning rate than the initial training.
D. Only fine tune the final layer of the IJ-Net model with the portrait dataset.
E. Retrain the entire diffusion model from scratch using the portrait dataset.

Answer: C

Explanation:
Fine-tuning both the CLIP model and the IJ-Net architecture is the most effective approach. The CLIP model needs to learn the semantic relationship between portrait-related text and images, and the U-Net needs to adapt to generating portraits instead of landscapes. Using a smaller learning rate prevents overfitting and allows the model to leverage its existing knowledge from the landscape dataset. Retraining from scratch is wasteful, and fine-tuning only one component may not be sufficient for good performance. Simply fine-tuning the last layer will not change much.

NEW QUESTION # 24
You are training a multimodal generative A1 model for image captioning. After initial training, you observe that the model excels at describing common objects but struggles with nuanced details and rare objects. Which of the following performance optimization strategies would be MOST effective in addressing this issue?

A. Implement a custom loss function that penalizes inaccuracies in describing rare objects more heavily.
B. Reduce the learning rate to fine-tune the model on the existing dataset.
C. Apply early stopping to prevent overfitting to the common objects.
D. Increase the number of layers in the encoder network.
E. Increase the batch size during training to improve GPU utilization.

Answer: A

Explanation:
Implementing a custom loss function is the most effective strategy because it directly addresses the model's weakness by focusing on accurate descriptions of rare objects. Increasing batch size improves training speed but not necessarily accuracy. Early stopping prevents overfitting, but doesn't specifically target the issue of rare object recognition. Reducing the learning rate might help with fine-tuning, but not as effectively as a targeted loss function. Increasing the number of layers may increase complexity but not guarantee better performance on rare objects.

NEW QUESTION # 25
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. Concatenate the raw text and tabular data into a single feature vector.
B. Use a Transformer-based model to encode the text and a separate neural network for the tabular data, then fuse the embeddings.
C. All of the above.
D. Convert the text data into numerical features using techniques like TF-IDF, then concatenate these features with the tabular data.
E. Train separate models for text and tabular data, then average their predictions.

Answer: B,D

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 # 26
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?

A. Decrease the resolution of the video frames.
B. Implement a contrastive learning objective that encourages similar embeddings for corresponding video frames and text descriptions.
C. Use a larger vocabulary for the text encoder.
D. Increase the frame rate of the training videos.
E. Using only pretrained model weights.

Answer: B

Explanation:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.

NEW QUESTION # 27
You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?

A. Exclusively train the model on articles with high-quality images to improve its visual processing capabilities.
B. Implement a modality dropout strategy during training, randomly masking either the text or image input to force the model to rely more on the available modality.
C. Reduce the weight of the image modality in the overall loss function.
D. Replace the image processing component with a simpler, less powerful model.
E. Increase the size of the training dataset by only adding more data with high quality images.

Answer: B

Explanation:
Modality dropout forces the model to learn robust representations from each modality independently, making it less reliant on the presence of both modalities. This improves performance when one modality is missing or of low quality. Training only on high-quality images (A) would exacerbate the problem. Reducing the image modality's weight (C) might help slightly but doesn't fundamentally address the issue. Using a simpler image model (D) would likely decrease overall performance. Increasing the training dataset size with only high-quality images (E) will not address the problem of the model's dependence on high-quality images.

NEW QUESTION # 28
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