NVIDIA Generative AI Multimodal Sample Questions:

1. Consider a multimodal generative model trained on a dataset of images and corresponding captions. After training, you observe that the model generates captions that are grammatically correct but often lack specific details and relevance to the input image. Which of the following regularization techniques is MOST likely to improve the faithfulness and informativeness of the generated captions?

A) Attention regularization to encourage the model to attend to relevant regions in the image when generating the caption.
B) KL divergence regularization to encourage the generated caption distribution to be similar to the prior caption distribution.
C) Dropout during training.
D) Adding Gaussian noise to the input images.
E) L1 regularization on the model weights.

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) 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.
B) Prioritizing speed and efficiency in the development process, neglecting to address potential biases, and deploying the model without conducting thorough testing or evaluation.
C) 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.
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) Focusing solely on improving the technical performance of the model, ignoring potential ethical concerns, and releasing the model as open-source to promote innovation.

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

4. Which of the following techniques are MOST likely to improve the energy efficiency of a large-scale multimodal AI model without significantly sacrificing accuracy?

A) Increasing the batch size during training.
B) Model quantization (e.g., converting weights from FP32 to INT8).
C) Using a larger, more complex model architecture.
D) Knowledge distillation to train a smaller student model.
E) Applying pruning techniques to remove less important connections in the model.

5. You are developing a multimodal generative model that takes a text description as input and generates a corresponding image. However, you notice that the generated images often lack fine-grained details and realism. Which of the following approaches could you employ to improve the quality and realism of the generated images? (Select all that apply)

A) Decrease the size of the text encoder.
B) Use a smaller training dataset.
C) Train the model using a generative adversarial network (GAN) framework.
D) Use a higher-resolution image generator architecture.
E) Implement a loss function that encourages the generated images to match the statistical distribution of real images.

Solutions: