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NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?

A) Using ReLU (Rectified Linear Unit) activation functions.
B) Using batch normalization.
C) Increasing the learning rate.
D) Employing skip connections (e.g., in ResNets).
E) Using sigmoid activation functions.

2. You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

A) Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').
B) Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
C) Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.
D) Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.
E) Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'

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

4. 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) Only utilize pre-trained open-source models
B) Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
C) 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.
D) 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.
E) Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.

5. Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?

A) Imputing missing MRI images using the average MRI image from the entire dataset.
B) Ignoring the MRI data completely and training the model only on the text and audio data.
C) Deleting all records with missing MRI images.
D) Using a masking approach during training, where the model is trained to predict the missing modality (MRI) from the available modalities (text and audio) for incomplete records and is trained with all modalities for complete records.
E) Training a separate model only on records with complete data and then using it to predict the missing data.

Solutions: