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NVIDIA Generative AI Multimodal Sample Questions (Q67-Q72):

NEW QUESTION # 67
Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

A. To resize all images to the same dimension.
B. To concatenate image and text data into a single tensor.
C. To create separate data loaders for images and text.
D. To ensure images and text are processed in the same order during training.
E. To create a custom dataset class for handling paired image and text data.

Answer: E

Explanation:
The code defines a custom dataset class ( 'ImageTextDataset' ) which is the standard way in PyTorch to handle datasets that involve paired data, such as images and corresponding text descriptions. This allows for efficient loading and processing of the data during training. The snippet does not directly concatenate, ensure order, or specifically resize the images, though these could be parts of the larger system built upon the dataset class. It also doesn't create separate data loaders, but allows to create one dataset class and loader for the multimodal data.

NEW QUESTION # 68
You are building a multimodal generative model that combines text and images. The goal is to generate realistic images based on textual descriptions. You have access to a pre-trained language model (e.g., BERT) and a pre-trained image generation model (e.g., StyleGAN). Which of the following architectures would be MOST suitable for effectively integrating these two models to achieve your objective?

A. Using the language model to generate a latent vector that is then fed into the image generation model as input.
B. Using the language model to generate captions for the images, and then training the image generation model on the captions.
C. Concatenating the text and image data into a single vector and feeding it into a standard feedforward neural network.
D. Fine-tuning the language model to directly output pixel values for the image.
E. Training a separate neural network to map the image to the text description.

Answer: A

Explanation:
Using the language model to generate a latent vector that serves as input to the image generation model is an effective approach for multimodal integration. This allows the language model to encode the textual description into a meaningful representation that can guide the image generation process. Fine-tuning the language model to output pixel values directly is not feasible due to the high dimensionality of images. Training a separate network to map images to text is a reverse task. Concatenating text and image data may not effectively capture the complex relationships between modalities. Generating captions for images is not the primary objective.

NEW QUESTION # 69
You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?

A. KL Divergence
B. Mean Squared Error (MSE)
C. Cross-entropy loss
D. Triplet Loss
E. Contrastive Loss

Answer: D

Explanation:
Triplet Loss is specifically designed for ranking tasks. It takes three inputs: an anchor (text query), a positive example (relevant image), and a negative example (irrelevant image). The loss function aims to minimize the distance between the anchor and the positive example while maximizing the distance between the anchor and the negative example. Contrastive loss works with pairs, not relative rankings. Cross-entropy, MSE, and KL Divergence are not suitable for ranking problems.

NEW QUESTION # 70
You have a dataset of customer reviews for a Generative A1 service. The dataset contains text reviews, numerical ratings (1-5 stars), and categorical data about the customer's subscription plan (Basic, Premium, Enterprise). You want to build a model to predict the numerical rating based on the text review and subscription plan. Which data analysis and modeling approach would be MOST suitable?

A. Use a decision tree to predict the numerical rating based on the text reviews (using TF-IDF) and subscription plan.
B. Train a deep learning model (e.g., BERT or RoBERTa) on the text reviews, concatenate the output embeddings with the one-hot encoded subscription plan, and use a regression layer to predict the numerical rating.
C. Perform sentiment analysis on the text reviews, then use linear regression to predict the numerical rating based on the sentiment score and subscription plan (one-hot encoded).
D. Calculate the average word length of the text reviews and use that as a feature in a linear regression model along with the subscription plan to predict the rating.
E. Use topic modeling on the text reviews, then use logistic regression to predict the numerical rating based on the topic distributions and subscription plan.

Answer: B

Explanation:
Using a pre-trained language model like BERT or RoBERTa captures the semantic meaning of the text reviews most effectively. Concatenating the embeddings with the subscription plan allows the model to learn the combined effect of both inputs. Regression layer is used as numeric ratings (1-5 stars) are provided as the target values. Sentiment and topic modeling can work as features but BERT/RoBERTa gives better context. Other options aren't able to capture complex context.

NEW QUESTION # 71
Which of the following techniques can be used to reduce the computational cost and memory footprint of large language models (LLMs) during inference?

A. Increasing the model size
B. Knowledge Distillation
C. Quantization
D. Adding more layers
E. Pruning

Answer: B,C,E

Explanation:
Quantization reduces the precision of the model's weights and activations, which can significantly reduce memory footprint and speed up inference. Knowledge distillation involves training a smaller 'student' model to mimic the behavior of a larger 'teacher' model, reducing the computational cost. Pruning removes unimportant connections (weights) from the model, leading to a sparser network and lower computational requirements. Increasing the model size or adding more layers would increase the computational cost.

NEW QUESTION # 72
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