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NVIDIA Generative AI Multimodal Sample Questions:

1. You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A) Increase the weight decay (L2 regularization).
B) Increase the learning rate.
C) Increase the training data size by collecting more data.
D) Decrease the learning rate.
E) Implement early stopping based on the validation loss.

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

3. You are evaluating a generative A1 model for image captioning. The model produces captions that are grammatically correct but often miss key objects in the image. Which of the following evaluation metrics would be MOST suitable to identify this deficiency?

A) Perplexity
B) CIDEr (Consensus-based Image Description Evaluation)
C) ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
D) Inception Score
E) BLEU (Bilingual Evaluation Understudy)

4. You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A) Add dropout layers to the model architecture.
B) Increase the batch size.
C) Implement weight decay (L2 regularization).
D) Decrease the learning rate.
E) Increase the size of the training dataset.

5. You are working on a project that involves generating music from video. The approach uses a pre-trained video encoder and a pre- trained music decoder. You find that the generated music often lacks a clear connection to the visual content of the video. To improve the coherence between the video and the generated music, which of the following steps would be the MOST effective? (Select TWO)

A) Fine-tune the entire system end-to-end with a loss function that encourages temporal alignment between video and music features.
B) Only use videos that are shorter than 5 seconds.
C) Train the video encoder and music decoder separately on larger datasets.
D) Remove the video encoder and generate music directly from random noise.
E) Introduce a cross-modal attention mechanism to allow the music decoder to attend to relevant visual features during music generation.

Solutions: