試験Databricks-Machine-Learning-Professional トピック6 問題92 スレッド

Databricks Databricks-Machine-Learning-Professionalのリアル試験問題集
問題 #: 92
トピック #: 6

A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

A. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.

B. Write basket data to a feature table and publish to the online store for low-latency access.

C. Store basket data in a Delta table and configure automatic feature lookup during model serving.

D. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.

おすすめの解答：A 解答を投票する

Real-time basket contents are request-specific and only available at inference time, making them unsuitable for precomputed feature tables or online stores. Implementing a custom pyfunc model allows the predict() method to directly consume and process the basket contents included in the request payload, enabling real-time feature engineering while keeping the logic tightly coupled with model inference in Databricks Model Serving.

Minai 2026-06-09 10:55:45

時間限定で

15%

オフ

プレミアムのDatabricks-Machine-Learning-Professional問題集のセルフテストエンジンまたはPDFをゲットしよう

弊社を連絡する

我々は１２時間以内ですべてのお問い合わせを答えます。

オンラインサポート時間：( UTC+9 ) 9:00-24:00
月曜日から土曜日まで

サポート：現在連絡

試験Databricks-Machine-Learning-Professional トピック6 問題92 スレッド

コメント

弊社を連絡する

関連リンク

トップ試験