Jeff’s Note #
Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.
For AWS DVA-C02 candidates, the confusion often lies in how to efficiently manage data lifecycle without causing throttling on throughput-limited DynamoDB tables. In production, this is about knowing exactly how DynamoDB TTL works natively versus manual deletion’s impact on provisioned capacity. Let’s drill down.
The Certification Drill (Simulated Question) #
Scenario #
BrightSpark Labs develops a real-time trivia platform for social events. For each completed trivia event, BrightSpark generates a leaderboard ranking all participants based on their scores. These leaderboard records are stored in a DynamoDB table configured with a fixed write capacity (provisioned mode). BrightSpark retains leaderboard data exactly 30 days after the event concludes, then removes expired records using a scheduled batch cleanup process. During heavy event months, when multiple deletes run concurrently, DynamoDB throttles write requests, slowing the leaderboard updates and user interactions.
The Requirement: #
BrightSpark Labs needs a scalable, long-term solution that automatically deletes old leaderboard data without causing write request throttling on the DynamoDB table.
The Options #
- A) Configure a DynamoDB TTL (Time to Live) attribute on the leaderboard table items.
- B) Use DynamoDB Streams to trigger a scheduled Lambda that deletes expired leaderboard data.
- C) Use AWS Step Functions to orchestrate the scheduled deletion of leaderboard data.
- D) Temporarily increase the table’s write capacity during the scheduled deletion batch job.
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Correct Answer #
A) Configure a DynamoDB TTL (Time to Live) attribute on the leaderboard table items.
Quick Insight: The Developer Imperative #
DynamoDB TTL is a native, server-side mechanism that handles data expiration automatically and asynchronously, significantly reducing the need for manual delete operations that consume write capacity and trigger throttling.
Content Locked: The Expert Analysis #
You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?
The Expert’s Analysis #
Correct Answer #
Option A
The Winning Logic #
DynamoDB TTL enables automatic expiry of items by specifying a timestamp attribute. Once the expiry time passes, DynamoDB asynchronously deletes the item without impacting table write capacity. This removes the need for a scheduled batch process that performs explicit delete API calls, which consume write capacity and can cause throttling under load. TTL also scales seamlessly during peak usage, making it ideal for event-driven leaderboard data with fixed retention periods.
The Trap (Distractor Analysis): #
- Why not B? Using DynamoDB Streams to trigger deletion still invokes delete API calls, consuming write capacity and can cause throttling during heavy load. This is a reactive solution but does not prevent write request spikes.
- Why not C? AWS Step Functions can orchestrate deletion jobs but ultimately depend on delete operations that use write capacity. It adds orchestration overhead without solving the throttling root cause.
- Why not D? Dynamically increasing write capacity temporarily can reduce throttling but impacts cost and operational complexity. This is a band-aid, not a scalable best practice.
The Technical Blueprint #
# Example AWS CLI command to enable TTL on the DynamoDB table
aws dynamodb update-time-to-live \
--table-name BrightSparkLeaderboard \
--time-to-live-specification Enabled=true,AttributeName=expiryTimestamp
This command enables TTL on the attribute expiryTimestamp that holds the UNIX epoch time (in seconds) when each leaderboard item expires (e.g., 30 days after event end).
The Comparative Analysis #
| Option | API Complexity | Performance Impact | Use Case Suitability |
|---|---|---|---|
| A) TTL | Low | Minimal write overhead; async deletes | Best for automatic expiry and scaling |
| B) DynamoDB Streams | Medium | Causes write spikes due to manual deletes | Reactive, limited scalability |
| C) Step Functions | High | Adds orchestration latency and cost | Adds complexity without solving the core issue |
| D) Increase Write Capacity | Operational overhead | Costly and reactive scaling | Not cost-effective for sustained periods |
Real-World Application (Practitioner Insight) #
Exam Rule #
For the exam, always pick DynamoDB TTL when you see a problem statement about predictable, time-based data expiration that requires automated deletion without manual interference.
Real World #
In reality, we might combine TTL with DynamoDB Streams for audit or archival workflows, but TTL remains the core best practice for efficient lifecycle management.
(CTA) Stop Guessing, Start Mastering #
Disclaimer
This is a study note based on simulated scenarios for the AWS DVA-C02 exam.