試験AIF-C01-JPN トピック3 問題58 スレッド
Amazon AIF-C01-JPNのリアル試験問題集
問題 #: 58
トピック #: 3
問題 #: 58
トピック #: 3
ある会社では、ドメイン固有のモデルを使用しています。この会社は、最初から新しいモデルを作成することを避けたいと考えています。代わりに、事前トレーニング済みのモデルを適応させて、新しい関連タスク用のモデルを作成したいと考えています。
どの ML 戦略がこれらの要件を満たしていますか?
どの ML 戦略がこれらの要件を満たしていますか?
おすすめの解答：B 解答を投票する
Transfer learning is the correct strategy for adapting pre-trained models for new, related tasks without creating models from scratch.
* Transfer Learning:
* Involves taking a pre-trained model and fine-tuning it on a new dataset for a related task.
* This approach is efficient because it leverages existing knowledge from a model trained on a large dataset, requiring less data and computational resources than training a new model from scratch.
* Why Option B is Correct:
* Adaptation of Pre-trained Models: Allows for adapting existing models to new tasks, which aligns with the company's goal of not starting from scratch.
* Efficiency and Speed: Speeds up the model development process by building on the knowledge of pre-trained models.
* Why Other Options are Incorrect:
* A. Increase the number of epochs: Does not address the strategy of reusing pre-trained models.
* C. Decrease the number of epochs: Similarly, does not apply to adapting pre-trained models.
* D. Use unsupervised learning: Does not involve using pre-trained models for new tasks.
* Transfer Learning:
* Involves taking a pre-trained model and fine-tuning it on a new dataset for a related task.
* This approach is efficient because it leverages existing knowledge from a model trained on a large dataset, requiring less data and computational resources than training a new model from scratch.
* Why Option B is Correct:
* Adaptation of Pre-trained Models: Allows for adapting existing models to new tasks, which aligns with the company's goal of not starting from scratch.
* Efficiency and Speed: Speeds up the model development process by building on the knowledge of pre-trained models.
* Why Other Options are Incorrect:
* A. Increase the number of epochs: Does not address the strategy of reusing pre-trained models.
* C. Decrease the number of epochs: Similarly, does not apply to adapting pre-trained models.
* D. Use unsupervised learning: Does not involve using pre-trained models for new tasks.
Uchida 2025-02-03 07:39:44
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