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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a system that uses text and images to generate 3D models. The text describes the object, and the images provide visual details. During training, you observe that the model heavily relies on the image input and largely ignores the text description. What technique can you employ to encourage the model to give more weight to the textual input?

A) Increase the resolution of the input images.
B) Decrease the learning rate for the image processing branch of the model.
C) Use a curriculum learning approach, starting with simpler text descriptions and gradually increasing the complexity.
D) Increase the size of the text vocabulary.
E) Apply a higher dropout rate to the image embedding layer.

2. Explain the role of Tensor Cores and mixed-precision training (e.g., using FP16 or bfloat16) in accelerating the training of large generative AI models.

A) Mixed-precision training allows using lower precision for forward and backward passes but keeps weights and gradients in higher precision to maintain stability.
B) A and B.
C) Mixed-precision training guarantees the same convergence behavior as full-precision training.
D) Tensor Cores perform specialized matrix multiplications optimized for lower-precision data types, enabling faster computation and reduced memory footprint.
E) Tensor Cores are only useful for inference, not training.

3. You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?

A) ROC curves (Receiver Operating Characteristic curves)
B) t-SNE (t-distributed Stochastic Neighbor Embedding)
C) Attention Heatmaps
D) Confusion Matrix
E) PCA (Principal Component Analysis)

4. You are working with a transformer-based multimodal model that processes both text and audio. You want to implement an efficient attention mechanism that reduces the computational cost associated with attending to the entire input sequence. Which of the following attention mechanisms would be MOST suitable for achieving this goal?

A) Scaled Dot-Product Attention
B) Global Attention
C) Sparse Attention
D) Multi-Head Attention
E) Local Attention

5. In a multimodal emotion recognition system, you are using both facial expressions and text messages as input. You observe that the model performs significantly better on individuals with clearly expressed facial emotions but poorly on individuals with subtle or masked facial expressions. Which of the following approaches would MOST directly address this bias?

A) Train a separate model specifically for individuals with subtle facial expressions.
B) Apply data augmentation techniques to the text message modality.
C) Increase the weight of the facial expression modality in the loss function.
D) Implement adversarial training to make the facial expression encoder invariant to expression intensity.
E) Collect more data from individuals with clearly expressed facial emotions.

Solutions: