Which TWO approaches can be used to optimize costs for an Amazon DynamoDB table with predictable read/write patterns? (Select TWO.)
Provisioned capacity with auto scaling is the most cost-effective approach for predictable workloads. DynamoDB uses CloudWatch alarms on utilization metrics (e.g., 70% of consumed capacity) to automatically increase or decrease your provisioned read and write capacity units, so you only pay for what your traffic actually requires. However, note that scaling happens gradually, so you must set sensible minimums and maximums to avoid both throttling and underused capacity.
Why this answer
Optimizing costs for DynamoDB with predictable workloads involves avoiding over-provisioning and reducing read/write consumption. Provisioned capacity with auto scaling (B) adjusts capacity based on actual usage, preventing unnecessary spending on unused capacity. DynamoDB Accelerator (DAX) (D) caches frequent reads, reducing read capacity unit consumption.
Option A (increasing RCU) leads to over-provisioning and higher costs. Option C (global tables) adds replication costs. Option E (on-demand) is more expensive than provisioned for predictable patterns.