Amazon DynamoDB is being used by an online shop to store data about client transactions. The table's entries include num

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Amazon DynamoDB is being used by an online shop to store data about client transactions. The table's entries include numerous text properties that describe the transaction, as well as a JSON attribute that provides the shopping cart and other transaction-related information. The average item is 250KB in size, with the majority of it being connected with the JSON property. Monthly, the typical user produces 3GB of data.

Customers use this table to see their transaction history and to review transaction details as necessary.
Ninety percent of the table's queries are performed when the transaction history view is constructed, with the remaining ten percent fetching transaction information. The table is partitioned by CustomerID and sorted according to the date of the transaction.

The customer has allocated a large amount of read capacity to the table and reports extremely even usage, but laments the expense of Amazon DynamoDB in comparison to alternative NoSQL options.

Which approach will minimize the cost of reading client requests without sacrificing quality?

A. Modify all database calls to use eventually consistent reads and advise customers that transaction history may be one second out-of-date.
B. Change the primary table to partition on TransactionID, create a GSI partitioned on customer and sorted on date, project small attributes into GSI, and then query GSI for summary data and the primary table for JSON details.
C. Vertically partition the table, store base attributes on the primary table, and create a foreign key reference to a secondary table containing the JSON data. Query the primary table for summary data and the secondary table for JSON details.
D. Create an LSI sorted on date, project the JSON attribute into the index, and then query the primary table for summary data and the LSI for JSON details.