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NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?

A) Using ReLU (Rectified Linear Unit) activation functions.
B) Using batch normalization.
C) Using sigmoid activation functions.
D) Employing skip connections (e.g., in ResNets).
E) Increasing the learning rate.

2. Consider the following Python code snippet used for processing image and text data for a multimodal model:

What is the primary limitation of the text encoding method used in this code, and how could it be improved for use in a real-world multimodal model?

A) The text encoding is efficient but incompatible with common deep learning architectures.
B) The text encoding only supports ASCII characters and does not account for word embeddings or sequence length variations. Use a tokenizer like BERT or SentencePiece to generate embeddings and pad sequences to a fixed length
C) It adequately addresses the complexities inherent in natural language, making it suitable for a variety of multimodal models.
D) The text encoding is overly complex and should be simplified to reduce computational overhead.
E) The text encoding is suitable for small datasets but will not scale to larger datasets.

3. You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A) Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
B) Randomly shuffle the training dataset to minimize bias.
C) Filter out all examples containing demographic information from the training dataset.
D) Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
E) Increase the size of the training dataset to dilute the effect of any biased examples.

4. You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?

A) Modality dropout: Randomly dropping out one of the modalities during training.
B) Adversarial training to make each modality indistinguishable.
C) Gradient blending: Adjusting the gradients from each modality based on their relative importance.
D) Reweighting the loss function to penalize errors made based on the less dominant modality (image).
E) Increasing the complexity of the audio processing branch and simplifying the image processing branch of the model.

5. Consider the following scenario: You are building a multimodal system for autonomous driving that uses both camera images and LiDAR data to perceive the environment. The LiDAR data is sparse and noisy, while the camera images are rich in visual details but can be affected by lighting conditions. Which of the following fusion strategies is MOST robust and effective for combining these two modalities?

A) Rely solely on the camera images and discard the LiDAR data to simplify the system.
B) Rely solely on the LiDAR data and discard the camera images to avoid lighting problems.
C) Fuse the raw sensor data directly by concatenating the LiDAR point cloud and the image pixels.
D) Use an early fusion approach where the features extracted from each modality are combined at an early stage of the processing pipeline, allowing the model to learn cross-modal interactions.
E) Train separate models for each modality and combine their predictions at the end using a simple averaging scheme.

Solutions: