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NVIDIA Generative AI Multimodal Sample Questions:

1. You're developing a system that analyzes video footage and generates textual summaries of the events occurring in the video. Which of the following architectures would be the MOST appropriate starting point for this task?

A) A purely convolutional neural network (CNN) for image classification.
B) A long short-term memory (LSTM) network for time series forecasting.
C) A support vector machine (SVM) for classification.
D) A generative adversarial network (GAN) for image generation.
E) A transformer-based encoder-decoder architecture, with the encoder processing video frames (e.g., using a 3D CNN) and the decoder generating text.

2. You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

A) Use a negative prompt to exclude unwanted elements or styles.
B) Decrease the CFG (Classifier-Free Guidance) scale.
C) Change the random seed to explore different variations.
D) Reduce the number of inference steps.
E) Increase the number of inference steps.

3. 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) BLEU score (or similar text generation metric) for generated descriptions.
C) Model size on disk.
D) Inference latency (time per request).
E) GPU utilization.

4. Consider the following code snippet used in training a multimodal model:

During experimentation, you discover that the image modality contributes negligibly to the final prediction. How would you modify the training loop to dynamically adjust the importance of each modality?

A) Introduce a modality dropout mechanism that randomly drops either the image or text modality during each training iteration.
B) Implement a separate loss function for the image modality and adjust its weight based on validation performance.
C) Compute modality-specific gradients and apply a scaling factor to the image gradients based on their magnitude relative to the text gradients.
D) Use a curriculum learning approach where the model is initially trained only on the text modality, and the image modality is gradually introduced.
E) Apply a fixed weight to the image features before feeding them into the model.

5. Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:

Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)

A) combined_embedding_weighted has shape (32, 512).
B) combined_embedding_weighted has shape (32, 1024).
C) combined_embedding_add has shape (32, 1024).
D) combined_embedding_concat has shape (64, 512).
E) combined_embedding_concat has shape (32, 1024).

Solutions: