AWS Certified Big Data - Specialty (#2)

An online retailer is using Amazon DynamoDB to store data related to customer transactions. The items in the table contains several string attributes describing the transaction as well as a JSON attribute containing the shopping cart and other details corresponding to the transaction. Average item size is – 250KB, most of which is associated with the JSON attribute. The average customer generates – 3GB of data per month. Customers access the table to display their transaction history and review transaction details as needed. Ninety percent of the queries against the table are executed when building the transaction history view, with the other 10% retrieving transaction details. The table is partitioned on CustomerID and sorted on transaction date. The client has very high read capacity provisioned for the table and experiences very even utilization, but complains about the cost of Amazon DynamoDB compared to other NoSQL solutions. Which strategy will reduce the cost associated with the client’s read queries while not degrading quality?

Modify all database calls to use eventually consistent reads and advise customers that transaction history may be one second out-of-date.
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.
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.
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.

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