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A data scientist has created a Python functioncompute_featuresthat returns a Spark DataFrame with the following schema:
The resulting DataFrame is assigned to thefeatures_dfvariable. The data scientist wants to create a Feature Store table usingfeatures_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Clientfs?
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?