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NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following techniques are MOST relevant to optimizing the energy efficiency of a large multimodal generative A1 model deployed on NVIDIA GPUs? (Select TWO)

A) Adding more data augmentation techniques to the training process.
B) Using mixed precision training (e.g., FP16) to reduce memory usage and computation.
C) Implementing model parallelism across multiple GPUs without optimizing communication overhead.
D) Increasing the size of the hidden layers in the transformer architecture.
E) Knowledge distillation, transferring the knowledge to a smaller model.

2. You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?

A) Downsampling the video footage to reduce computational cost.
B) Normalizing the time-series data to a consistent range.
C) All of the above.
D) Converting the video footage to grayscale to simplify feature extraction.
E) Synchronizing the timestamps of the time-series data and video frames.

3. Consider the following PyTorch code snippet for a multimodal loss function:

What is the MOST significant issue with this code, preventing it from working as intended for a multimodal task?

A) The code lacks normalization of image and text features before computing the loss.
B) The code uses 'CrossEntropyLosS , which is not suitable for feature vectors but for classification scores.
C) The code doesn't include any regularization to prevent overfitting.
D) The 'alpha' parameter is not being used correctly to balance the image and text losses.
E) The function only works for a specific batch size.

4. You're building a multimodal model that takes images and text as input. You notice that your model is heavily biased towards the text modality, essentially ignoring the visual input. Which of the following strategies could you employ to address this modality imbalance? (Select TWO)

A) Implement a gating mechanism that dynamically adjusts the contribution of each modality based on the input.
B) Remove the text modality entirely.
C) Use a modality-specific loss function, weighting the loss from the visual modality more heavily.
D) Increase the learning rate for the text encoder.
E) Reduce the size of the visual encoder.

5. You are working on a multimodal emotion recognition system that analyzes video (visual and audio) and transcript (text) dat a. You want to fuse these modalities effectively. Which fusion technique is MOST likely to capture complex inter-modal relationships and improve performance, especially when the modalities have varying degrees of reliability?

A) Late fusion (averaging the probabilities from separate modality-specific models).
B) Early fusion (concatenating features before feeding into a single model).
C) Feature-level averaging.
D) Simple concatenation of modality-specific embeddings at a single point in the model.
E) Attention-based fusion (using attention mechanisms to weigh the contributions of each modality dynamically).

Solutions: