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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot's performance on complex visual scenes?

A) Reduce the resolution of the input images.
B) Remove the image processing component entirely.
C) Integrate an object detection model to identify and localize objects in the image before feeding the information to the chatbot.
D) Train the chatbot on a dataset with only simple images containing a single object.
E) Use a larger language model for the chatbot.

2. You are training a Generative Adversarial Network (GAN) to generate realistic images. After several epochs, you observe that the generator is consistently producing similar images, regardless of the input noise. This phenomenon is known as:

A) Vanishing gradients
B) Exploding gradients
C) Overfitting
D) Underfitting
E) Mode collapse

3. Consider the following PyTorch code snippet used for training a Generative A1 model:

A) The code is correct and will train the model efficiently.
B) CUDAOOM error because gradients are accumulating without updating parameters.
C) The learning rate scheduler is not being used correctly.
D) The code will run, but it's computationally inefficient. Gradients should be zeroed before each backward pass.
E) The model parameters will not be updated correctly since optimizer.step() is called outside the loop.

4. You are tasked with building a multimodal generative A1 model that takes an image and a text prompt as input and generates a corresponding audio description. The image data is processed with a Vision Transformer (ViT), the text prompt is processed with a Transformer, and you need to fuse these modalities to generate the audio. Which of the following fusion strategies would be MOST appropriate for this task, considering the need for coherent and contextually relevant audio generation?

A) Concatenate the final hidden states of the ViT and the Transformer and feed them into a fully connected layer to generate audio features.
B) Train separate models for image-to-audio and text-to-audio and then average their predicted audio features.
C) Fine-tune a pre-trained text-to-audio model using the image features as a conditioning signal.
D) Use a cross-attention mechanism where the ViT's feature maps attend to the Transformer's hidden states at multiple layers.
E) Apply a simple addition or element-wise multiplication to the final hidden states of the ViT and the Transformer.

5. You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A) Number of lines of code in the model.
B) GPU utilization.
C) Inference latency (time per request).
D) BLEU score (or similar text generation metric) for generated descriptions.
E) Model size on disk.

Solutions: