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NVIDIA Generative AI Multimodal Sample Questions (Q257-Q262):

NEW QUESTION # 257
You are working with a dataset of handwritten digits and training a Variational Autoencoder (VAE) to generate new digits. After training, you observe that the generated digits are blurry and lack sharp details. Which of the following modifications could potentially improve the quality of the generated digits in your VAE?

A. Reducing the weight of the KL divergence term in the VAE loss function.
B. Using a simpler decoder architecture.
C. Increasing the weight of the KL divergence term in the VAE loss function.
D. Increasing the capacity of the encoder and decoder networks (e.g., adding more layers or neurons).
E. Decreasing the dimensionality of the latent space.

Answer: A,D

Explanation:
Increasing the capacity of the encoder and decoder allows the VAE to learn more complex representations of the data. Reducing the weight of the KL divergence term allows the model to prioritize reconstruction accuracy, which can lead to sharper details. Decreasing latent space dimensionality might restrict the model's ability to capture fine-grained details. A simpler decoder will lead to more blurry images. Increasing the KL divergence weight can lead to disentangled representations, but often at the cost of reconstruction quality (blurriness).

NEW QUESTION # 258
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. 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.
D. Force the model to generate only scenes that exist within the training set.
E. 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.

Answer: A,C,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 # 259
You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model's robustness and accuracy?

A. Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
B. Imputing missing values using simple methods like mean imputation or filling with a constant value.
C. Training seperate models for each avalible modality.
D. Removing all patients with missing data to create a clean dataset.
E. Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.

Answer: A,E

Explanation:
Removing patients with missing data can lead to a significant loss of information and bias the model. Simple imputation methods can introduce inaccuracies and fail to capture the relationships between modalities. Multimodal variational autoencoders (MVAEs) are specifically designed to handle missing data in multimodal datasets by learning a joint latent representation and imputing values based on the observed modalities. This approach is more robust and accurate than simple imputation methods. GAN can also be used to impute missing values.

NEW QUESTION # 260
You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A. Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
B. Only evaluate on a subset of the test data that closely resembles the training data.
C. Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.
D. Reduce the size of the model to prevent overfitting to the training data.
E. Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.

Answer: A

Explanation:
Domain adaptation techniques (C) are specifically designed to address domain shift by learning features that are invariant to the source and target domains. Data augmentation (A) can help but might not be sufficient. Fine-tuning on test data (B) is data leakage and invalidates the test set. Reducing model size (D) may not address the core issue of domain shift. Selecting a subset of the test data (E) defeats the purpose of testing generalization.

NEW QUESTION # 261
You're working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?

A. Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.
B. Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.
C. Training the model on a larger dataset of music.
D. Increasing the size of the model's hidden layers.
E. Adding more layers to the model.

Answer: A,B

Explanation:
A VAE can learn a structured latent space that captures essential musical features, allowing for controlled generation. RNNs with attention are well-suited for modeling sequential data like music, capturing long-range dependencies and creating a more coherent structure. Simply increasing the size or depth of the model may not address the underlying issue of musical structure. A larger dataset may help, but structured modeling techniques are generally more effective.

NEW QUESTION # 262
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