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NVIDIA Generative AI Multimodal Sample Questions (Q185-Q190):

NEW QUESTION # 185
You are training a multimodal model to predict stock prices using news articles (text) and historical price charts (images). You notice the model is overfitting to the historical price charts and largely ignoring the news articles. What is a potential solution to mitigate this overfitting?

A. Increase the learning rate for the image processing component of the model.
B. Apply stronger regularization (e.g., dropout, Ll/L2 regularization) to the image processing component and/or increase the weight of the text-based loss function.
C. Use a simpler model architecture for processing text.
D. Remove the image data entirely to prevent overfitting.
E. Reduce the batch size for the text data.

Answer: B

Explanation:
Overfitting to the image data suggests that the model is learning the patterns in the price charts too well, without generalizing to unseen data. Applying stronger regularization to the image processing component and/or increasing the weight of the text-based loss function will encourage the model to pay more attention to the news articles and reduce its reliance on the price charts. This encourages a more balanced learning process.

NEW QUESTION # 186
You have trained a text-to-image diffusion model. During inference, you notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques could you apply to improve the image quality without retraining the model?

A. Increase the model's capacity by adding more layers.
B. Reduce the learning rate during sampling.
C. Use a larger batch size during inference.
D. Increase the number of diffusion steps during sampling.
E. Decrease the guidance scale during sampling.

Answer: D

Explanation:
Increasing the number of diffusion steps during sampling allows the model to refine the generated image more thoroughly, leading to finer details and reduced blurriness. The guidance scale controls how closely the generated image adheres to the input text prompt; increasing it typically improves adherence but can sometimes reduce diversity. Batch size primarily affects computational efficiency. Reducing the learning rate is relevant during training, not inference. Adding model layers requires retraining.

NEW QUESTION # 187
Which data augmentation techniques are MOST suitable for improving the robustness of a multimodal model that uses images and text?

A. Changing the image resolution and increasing the text size.
B. Rotating images and back-translating text (translating to another language and back to the original).
C. Randomly cropping images and translating text to different languages.
D. Adding Gaussian noise to images and randomly deleting words in the text.
E. Rescaling images and changing the font of the text.

Answer: B,D

Explanation:
Rotating images and back-translating text are effective as they introduce variations that the model might encounter in real-world scenarios. Adding Gaussian noise and randomly deleting words helps the model become more robust to noisy or incomplete data. Cropping and translation, while augmentation techniques, don't specifically target multimodal robustness as effectively. Changing resolution and font are less likely to generalize well.

NEW QUESTION # 188
You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

A. Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.
B. Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.
C. Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
D. Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.
E. Only utilize pre-trained open-source models

Answer: A,B,D

Explanation:
Differential privacy (A) adds noise to the data to protect individual records. On-premises deployment (B) maintains control over data access. Federated learning (D) trains the model on decentralized data without centralizing it. Avoiding client data entirely (C) may limit the model's effectiveness. NVIDIA Merlin and FLARE are tools that provide methods to create safe and private architecture. (E) is not always the best approach since the model might be very generalized and not adapted to specific tasks.

NEW QUESTION # 189
You are working with a large multimodal dataset containing images and text. You want to efficiently load and preprocess this data for training a generative A1 model on an NVIDIA GPU. Which of the following approaches would be most effective for maximizing data loading speed and GPU utilization?

A. Using a Python-based data loader that reads images and text directly from disk during training.
B. Compressing the dataset into a single large archive file and decompressing it on the fly during training.
C. Employing NVIDIA's DALI (Data Loading Library) to perform data loading and preprocessing on the GPU.
D. Storing the images and text in a relational database and querying the database during training.
E. Loading the entire dataset into CPU memory before starting training.

Answer: C

Explanation:
NVIDIA DALI is specifically designed for accelerating data loading and preprocessing on NVIDIA GPUs. It allows you to perform tasks like image decoding, resizing, and data augmentation directly on the GPIJ, minimizing CPIJ overhead and maximizing GPU utilizatiom Loading the entire dataset into CPU memory is impractical for large datasets. Python-based data loaders can be slow due to the GIL (Global Interpreter Lock). Querying a relational database adds overhead. Compressing the dataset can save storage space but may introduce decompression bottlenecks during training.

NEW QUESTION # 190
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