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An e-commerce company has an application that uses Amazon DynamoDB tables configured with provisioned capacity. Order data is stored in a table named Orders. The Orders table has a primary key of order-ID and a sort key of product-ID. The company configured an AWS Lambda function to receive DynamoDB streams from the Orders table and update a table named Inventory. The company has noticed that during peak sales periods, updates to the Inventory table take longer than the company can tolerate. Which solutions will resolve the slow table updates? (Select TWO.)
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Key Problem:
Delayed Inventory table updates during peak sales.
DynamoDB Streams and Lambda processing require optimization.
Analysis of Options:
Option A: Adding a GSI is unrelated to the issue. It does not address stream processing delays or capacity issues.
Option B: Optimizing batch size reduces latency and allows the Lambda function to process larger chunks of data at once, improving performance during peak load.
Option C: Increasing write capacity for the Inventory table ensures that it can handle the increased volume of updates during peak times.
Option D: Increasing read capacity for the Orders table does not directly resolve the issue since the problem is with updates to the Inventory table.
Option E: Increasing Lambda timeout only addresses longer processing times but does not solve the underlying throughput problem.
AWS Reference:
DynamoDB Streams Best Practices
Provisioned Throughput in DynamoDB