試験NCA-GENM トピック1 問題272 スレッド

NVIDIA NCA-GENMのリアル試験問題集
問題 #: 272
トピック #: 1

You are training a multimodal generative A1 model that takes text and images as input to generate videos. During experimentation, you observe that the model performs well on common scenarios (e.g., 'a dog playing in the park') but struggles to generate coherent videos for less frequent or abstract scenarios (e.g., 'the concept of time flowing'). What is the MOST effective strategy to improve the model's performance on these challenging scenarios, focusing on test data quality?

A. Increase the size of the training dataset by duplicating existing common scenario examples.

B. Implement data augmentation techniques on the existing training data, focusing on color adjustments and minor image transformations.

C. Curate a new test dataset specifically containing challenging scenarios and use it to evaluate and fine-tune the model. Ensure the new test data includes diverse interpretations and variations of the abstract concepts.

D. Reduce the complexity of the model architecture to prevent overfitting on the common scenarios.

E. Train the model for a significantly longer duration on the existing training data.

おすすめの解答：C 解答を投票する

Curating a specific test dataset for challenging scenarios allows for targeted evaluation and fine-tuning. Duplicating common scenarios (A) won't address the problem. Simple augmentations (B) may not be sufficient. Reducing model complexity (D) might hurt overall performance. Longer training (E) without addressing data bias is unlikely to help significantly.

Kanno 2026-04-09 07:22:17

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試験NCA-GENM トピック1 問題272 スレッド

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