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NVIDIA Generative AI Multimodal Sample Questions:

1. You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.

A) Apply techniques like beam search during decoding to generate more diverse code snippets.
B) Increase the number of Transformer layers.
C) Increase the size of the vocabulary used by the model.
D) Use a reinforcement learning approach where the reward is based on whether the generated code passes the unit tests associated with the descriptiom
E) Fine-tune the model on a subset of the data where each code snippet is accompanied by unit tests, and train the model to also generate these tests.

2. You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?

A) Implement a perceptual loss function that compares high-level features of generated and real images.
B) Increase the batch size used for training.
C) Train the model for fewer epochs.
D) Use a simpler generator architecture.
E) Decrease the learning rate of the discriminator.

3. You are tasked with evaluating a text-to-video generation model. Which of the following metrics would be MOST appropriate for assessing the temporal coherence and smoothness of the generated videos?

A) Inception Score (IS)
B) BLEU score
C) Frchet Video Distance (FVD)
D) Learned Perceptual Image Patch Similarity (LPIPS)
E) Frchet Inception Distance (FID)

4. You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?

A) Triplet Loss
B) Cross-entropy loss
C) Contrastive Loss
D) KL Divergence
E) Mean Squared Error (MSE)

5. You are tasked with optimizing a multimodal A1 model that processes both images and text. You observe significant latency during the image encoding phase using a pre-trained ResNet50 model. Which of the following techniques would be MOST effective in reducing latency while preserving accuracy, considering energy efficiency?

A) Use full precision floating point operations throughout the ResNet50 model.
B) Apply knowledge distillation, training a smaller, faster model to mimic the ResNet50 output.
C) Disable GPU acceleration for image processing to reduce power consumption.
D) Replace ResNet50 with a larger, more complex model like ResNeXt101.
E) Increase the batch size for image processing.

Solutions: