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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a system that uses both video and text to determine the sentiment of movie reviews. You notice that while your system works great on the training set, the performance is much worse on the validation set. What is the MOST likely reason for this and what methods can you use to improve the performance?

A) The text data is corrupt. Clean the text data by ensuring that the text is not noisy or missing.
B) The model is not complex enough. Use a larger model or different model to improve results.
C) The model is overfitting on the training data. Use regularization techniques or more training data to overcome this.
D) The training data is not representative enough of the real world. Gather new data that matches the real world, or introduce a cross validation training routine.
E) The Video Data is too Large. Consider compressing the video data to ensure that it all fits into memory.

2. You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A) Only evaluate on a subset of the test data that closely resembles the training data.
B) Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.
C) Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
D) Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.
E) Reduce the size of the model to prevent overfitting to the training data.

3. You are working with a large dataset of images to train a Generative A1 model. You suspect that some images are corrupted or of poor quality, which could negatively impact training. Which of the following methods would be the MOST effective in identifying and removing these problematic images?

A) Calculate the average pixel intensity for each image and remove those with very low or very high average intensity.
B) Compute the image sharpness (e.g., using Laplacian variance) and remove images with low sharpness values.
C) Manually inspect each image and remove those that appear to be corrupted or low quality.
D) Use a pre-trained image quality assessment model (e.g., BRISQUE, NIQE) to score each image and remove those with low scores.
E) Check for file corruption errors during image loading and remove those files.

4. Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?

A) Imputing missing MRI images using the average MRI image from the entire dataset.
B) Ignoring the MRI data completely and training the model only on the text and audio data.
C) Deleting all records with missing MRI images.
D) Using a masking approach during training, where the model is trained to predict the missing modality (MRI) from the available modalities (text and audio) for incomplete records and is trained with all modalities for complete records.
E) Training a separate model only on records with complete data and then using it to predict the missing data.

5. Consider the following code snippet using NVIDIA Triton Inference Server. What is the purpose of the 'sequence_batching' configuration?

A) It automatically scales the number of model instances based on the input load.
B) It allows for processing sequences of inputs (e.g., time series data) by maintaining state between requests.
C) It enables batching of independent requests to improve throughput.
D) It enables dynamic batching based on request arrival times.
E) It optimizes the model for specific hardware architectures.

Solutions: