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NVIDIA Generative AI Multimodal Sample Questions:

1. You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A) Add dropout layers to the model architecture.
B) Decrease the learning rate.
C) Implement weight decay (L2 regularization).
D) Increase the size of the training dataset.
E) Increase the batch size.

2. You are working on a project involving generating photorealistic images of human faces using a generative model. Ethical considerations are paramount. Which of the following practices are MOST important to incorporate into your development workflow to mitigate potential biases and misuse?

A) Prioritizing speed and efficiency in the development process, neglecting to address potential biases, and deploying the model without conducting thorough testing or evaluation.
B) Implementing strict controls over the types of images the model can generate, limiting its use to specific applications, and restricting access to the model to a small group of trusted individuals.
C) Focusing solely on improving the technical performance of the model, ignoring potential ethical concerns, and releasing the model as open-source to promote innovation.
D) Using synthetic data for training to avoid any potential privacy concerns related to real-world data, ignoring potential biases in the synthetic data, and claiming that the model is completely unbiased.
E) Training the model on a diverse and representative dataset, implementing mechanisms to detect and mitigate biases in the generated images, and providing transparency about the limitations and potential risks of the technology.

3. You are using NeMo to fine-tune a large language model for a text-to-image task. During training, you encounter a CUDA out-of-memory error, despite using mixed-precision training. What is the MOST effective strategy to reduce memory consumption and continue training without significantly degrading model performance?

A) Disable mixed-precision training.
B) Enable gradient checkpointing.
C) Decrease the gradient accumulation steps.
D) Switch to a smaller model architecture.
E) Increase the batch size.

4. Given the following Python code snippet using Pandas, which is intended to filter rows where the 'price' column is greater than 100 and the 'quantity' column is less than 5, identify the correct approach to achieve this:

A)

B)

C)

D)

E)

5. You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

A) Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.
B) Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.
C) Deploying Riva on CPU using multiprocessing.
D) Deploying Riva on a single GPU with a large batch size.
E) Deploying Riva on a single CPU core with a small batch size.

Solutions: