NVIDIA NCA-GENM : NVIDIA Generative AI Multimodal

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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a multimodal application that takes an image and a short text description as input and generates a more detailed text description of the image. Which of the following model architectures is BEST suited for this task?

A) A Vision Transformer (ViT) for image encoding and a Transformer for text decoding.
B) A Multilayer Perceptron (MLP) for both image and text.
C) A Generative Adversarial Network (GAN) with separate image and text encoders.
D) A Recurrent Neural Network (RNN) with attention mechanisms.
E) A simple CNN followed by an LSTM.

2. Consider a scenario where you are developing a multimodal model for medical diagnosis using patient medical history (text), X-ray images, and ECG data (time-series). A significant portion of the ECG data is missing due to sensor malfunction. Which of the following approaches would be MOST effective in handling the missing data and ensuring accurate diagnosis?

A) Replace the missing ECG data with the average values from the entire dataset.
B) Combine imputation of missing ECG data with a robust multimodal fusion technique.
C) Train a separate model using only the available medical history and X-ray images, ignoring the ECG data altogether.
D) Impute the missing ECG values using time-series imputation techniques (e.g., Kalman filtering or interpolation).
E) Employ a multimodal fusion technique that is robust to missing modalities, such as attention mechanisms that dynamically weight the available data sources.

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

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

5. You're developing a real-time video captioning system. Latency is critical. Which of the following optimization strategies would provide the MOST significant reduction in end-to-end latency, assuming the captioning model is already optimized for inference?

A) Overlapping image feature extraction and caption generation using asynchronous execution.
B) Employing model quantization (e.g., converting weights from FP32 to INT8).
C) Caching the most frequent words in a local vocabulary.
D) All of the above.
E) Using a smaller batch size for inference.

Solutions: