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NVIDIA Generative AI Multimodal Sample Questions:

1. You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?

A) Reducing the number of layers in the Transformer.
B) Using only audio as input.
C) Removing the self-attention mechanism.
D) Using a smaller embedding dimension.
E) Incorporating positional encodings and increasing the attention window size.

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

3. You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

A) Fine-tuning the entire model with a small learning rate.
B) Using quantization to reduce the precision of the model's weights and activations.
C) Increasing the batch size to utilize the available memory more efficiently.
D) Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.
E) Freezing all layers of the pre-trained model and training only a small classification head.

4. You're building a multimodal model that processes both images and text. The image encoder outputs a feature vector of size 2048, and the text encoder outputs a feature vector of size 512. Which of the following strategies is MOST appropriate for combining these feature vectors before feeding them into a downstream classifier?

A) Average the two feature vectors, resulting in a combined vector of size 2048
B) Project both feature vectors into a common embedding space of size 2048 using separate linear layers, then average the resulting vectors.
C) Project both feature vectors into a common embedding space of size 256 using separate linear layers, then concatenate the resulting vectors.
D) Concatenate the two feature vectors directly, resulting in a combined vector of size 2560.
E) Project both feature vectors into a common embedding space of size 512 using separate linear layers, then concatenate the resulting vectors.

5. You're training a multimodal model for image and text retrieval. Given an image, the model should retrieve the most relevant text description from a database, and vice-vers a. You're using a dual-encoder architecture, where one encoder processes images and the other processes text, projecting them into a shared embedding space. What is the most effective way to train the model to ensure that semantically similar images and texts have close embeddings, while dissimilar ones have distant embeddings?

A) Use a simple L1 loss between the image and text embeddings-
B) Use a reconstruction loss that forces the model to reconstruct the input image from its text embedding and vice-versa.
C) Use a contrastive loss function that minimizes the distance between embeddings of matching image-text pairs and maximizes the distance between embeddings of non-matching pairs. Example: Triplet Loss, InfoNCE.
D) Apply adversarial training to make the embeddings indistinguishable between the two modalities.
E) Train the encoders independently using separate supervised tasks for image and text classification.

Solutions: