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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a multimodal model to generate captions for videos. You've noticed that your model struggles to capture temporal relationships and sequential dependencies in the video frames. Which of the following architectures or techniques would be BEST suited to address this?

A) A Multilayer Perceptron (MLP) trained on flattened video frames.
B) A combination of a CNN to extract features from individual frames, followed by a Recurrent Neural Network (RNN) like LSTM or GRU to model temporal dependencies between the extracted features.
C) A standard Convolutional Neural Network (CNN) applied independently to each frame.
D) Principal Component Analysis (PCA) to reduce dimensionality of each frame before feeding to the decoder
E) A 3D Convolutional Neural Network (3D CNN) that processes multiple frames as a volume.

2. Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

A)

B)

C)

D)

E)

3. You are fine-tuning a pre-trained multimodal model for a visual question answering (VQA) task. You notice that the model performs well on common questions but struggles with questions requiring reasoning about object relationships (e.g., 'Is the object to the left of the table bigger than the one on the table?'). What data augmentation technique would MOST likely improve performance on these challenging questions?

A) Generating synthetic images with varying object arrangements and relationships, paired with corresponding questions and answers.
B) Adding Gaussian noise to the images.
C) Rotating the images.
D) Randomly cropping the images.
E) Replacing objects in the images with other objects.

4. Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?

A) Krippendorff's Alpha
B) Spearman's rank correlation coefficient
C) Pearson correlation coefficient
D) Chi-squared test
E) Cohen's Kappa

5. You're building a multimodal model that integrates text, images, and audio. The text data has many missing values. Which of the following strategies would be MOST effective for handling missing text data while leveraging the other modalities?

A) Ignore the missing text values during training, assuming the model can learn from the available modalities.
B) Use a multimodal generative model (e.g., VAE, GAN) to impute the missing text based on the learned joint representation of all modalities.
C) Remove all data points with missing text values to ensure data integrity.
D) Use a simple imputation method like replacing missing text with a placeholder like 'unknown'.
E) Train a separate model to predict the missing text based on the available image and audio data, then impute the predicted values.

Solutions: