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NVIDIA Generative AI Multimodal Sample Questions (Q22-Q27):

NEW QUESTION # 22
You are building a multimodal generative A1 model that creates realistic indoor scenes by combining textual descriptions, floor plans (geospatial data), and object libraries. The goal is to generate high-quality 3D models of the scenes. However, the model often produces scenes with physically implausible object arrangements (e.g., objects floating in the air, overlapping furniture). How can you MOST effectively integrate physical constraints into the generation process to ensure more realistic scene compositions?

A. Train a separate discriminator network that evaluates the physical plausibility of generated scenes and penalizes implausible configurations during training.
B. Increase the size of the training dataset with more examples of realistic indoor scenes.
C. Force the model to generate only scenes that exist within the training set.
D. Implement a rule-based system that enforces basic physical constraints (e.g., objects must be supported by a surface, no object interpenetration) during the generation process.
E. Use a physics engine (e.g., NVIDIA PhysX) as a post-processing step to simulate the generated scene and correct any physically implausible object placements.

Answer: A,D,E

Explanation:
Using a physics engine for post-processing (B) directly simulates physical interactions. Implementing a rule-based system (C) enforces basic constraints. Training a discriminator (D) adds a learning component for physical plausibility. Increasing the dataset size (A) might help but doesn't guarantee physical plausibility. Limiting generation to the training set (E) restricts creativity and generalization.

NEW QUESTION # 23
You are working on a multimodal AI model that generates images from text descriptions. You notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques is LEAST likely to improve the visual quality of the generated images?

A. Increasing the latent space dimensionality of the generative model.
B. Using a perceptual loss function that penalizes differences in high-level features.
C. Reducing the batch size during training.
D. Employing a convolutional neural network (CNN) with strided convolutions for upsampling.
E. Training with a larger dataset of higher-resolution images.

Answer: C

Explanation:
Reducing the batch size during training often leads to more unstable training and can actually worsen the quality of generated images, especially in generative adversarial networks (GANs) or similar models. The other options (A, B, C, E) are all techniques commonly used to improve the detail and sharpness of generated images. Larger latent space allows more nuanced representations. Perceptual losses encourage the model to generate realistic-looking images. Improved upsampling avoids checkerboard artifacts. More high-res data helps.

NEW QUESTION # 24
You are evaluating two different generative A1 model architectures (Model A and Model B) for image generation. You use the Frechet Inception Distance (FID) score as your primary evaluation metric. Model A has a lower FID score than Model B. Which of the following statements are MOST accurate regarding the interpretation of the FID scores? (Select TWO)

A. Model B necessarily has better performance on downstream tasks using the generated images.
B. Model A generates images that have a distribution more similar to the real image distribution used for calculating the FID score.
C. Model A generates images that are more visually appealing to human observers.
D. Model A is less likely to suffer from mode collapse than Model B.
E. Model B generates images that are more diverse than Model A.

Answer: B,D

Explanation:
A lower FID score indicates that the generated images are statistically more similar to the real images (B). It also suggests that Model A is less prone to mode collapse (D), as it captures the data distribution better. FID score doesn't guarantee visual appeal (A) or better performance on downstream tasks (E). Diversity (C) isn't directly implied by a lower FID score alone.

NEW QUESTION # 25
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. 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).
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. 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.
D. Use topic modeling on the text reviews, then use logistic regression to predict the numerical rating based on the topic distributions and subscription plan.
E. Use a decision tree to predict the numerical rating based on the text reviews (using TF-IDF) 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 # 26
You are building a generative model that takes both image and text input to generate novel images. You are using a Variational Autoencoder (VAE) architecture with separate encoders for images and text. After training, you observe that the generated images are heavily influenced by the image input and barely incorporate the text information. Which of the following techniques would MOST likely improve the incorporation of text information into the generated images?

A. Using a cross-attention mechanism in the decoder to allow the image features to attend to the text features during image generatiom
B. Decreasing the capacity of the text encoder.
C. Increasing the capacity of the image encoder and decoder.
D. Train two separate VAE models. One for Text and another for images.
E. Removing the text encoder and only using the image encoder.

Answer: A

Explanation:
A cross-attention mechanism allows the image features to selectively attend to the relevant parts of the text features during the image generation process. This enables the model to effectively incorporate the text information into the generated images- Increasing the capacity of the image encoder/decoder might further bias the model towards the image input Decreasing the capacity of the text encoder would further reduce the influence of text. Removing the text encoder is obviously not a solution- Training two separate VAE models won't generate correlated Image and Text.

NEW QUESTION # 27
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