How does DynamoDB TTL prevent throttling during data cleanup?
A company built an online event platform. For each event, the company organizes quizzes and generates leaderboards that are based on the quiz scores. The company stores the leaderboard data in Amazon DynamoDB and retains the data for 30 days after an event is complete. The company then uses a scheduled job to delete the old leaderboard data. The DynamoDB table is configured with a fixed write capacity. During the months when many events occur, the DynamoDB write API requests are throttled when the scheduled delete job runs. A developer must create a long-term solution that deletes the old leaderboard data and optimizes write throughput. Which solution meets these requirements?
Community Votes
100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
This question tests knowledge of DynamoDB’s native expiration mechanism versus manual deletion workflows, highlighting the common trap of triggering write capacity throttling through scheduled batch deletes.
DynamoDB Time-To-Live (TTL) automatically expires and deletes items without consuming provisioned write capacity units. Exam candidates and the community universally recognize TTL as the most efficient, serverless approach for managing time-bound datasets like event leaderboards.
Option D is frequently selected by test-takers attempting to scale resources during peak periods, but it ignores the architectural benefit of offloading deletions and unnecessarily inflates operational costs.
Community Discussion (5 comments)
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Expert Analysis
Why the Answer Is Correct
Configuring a TTL attribute allows DynamoDB to asynchronously expire and delete items after the specified timestamp passes. These background deletions do not consume your table’s Write Capacity Units (WCU), completely eliminating the throttling issue caused by the scheduled cleanup job. This provides a fully managed, cost-effective, and scalable long-term solution that aligns perfectly with the requirement to optimize write throughput.Why the Other Options Are Wrong
DynamoDB Streams (Option B) capture data modification events but cannot independently schedule or trigger periodic deletions. AWS Step Functions (Option C) can orchestrate workflows, but executing bulk delete operations would still consume WCU and potentially cause the same throttling problem. Increasing write capacity temporarily (Option D) addresses the symptom rather than the root cause, leading to unnecessary costs and failing to meet the optimization requirement.Community Comment Notes
The voting distribution shows 100% agreement on Option A, reflecting strong community confidence. Multiple contributors explicitly note that “A | Always consider TTL when trying to ditch from DynamoDB,” reinforcing its status as a go-to pattern for data expiration. Additional comments simply confirm TTL as the correct mechanism, demonstrating that this is a well-established best practice among AWS developers.Official Reference
Exam Strategy
For any DynamoDB scenario involving automatic expiration, archival, or cleanup of time-sensitive data, prioritize TTL over custom Lambda jobs or cron-based scripts. Reserve manual deletion or capacity scaling only for non-expiring data or emergency overrides.
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