DBS-C01 Workload-Specific Database Design Practice Question
A company runs a multi-tenant SaaS application on Amazon DynamoDB. Each tenant's data is stored in a separate table named with a tenant-specific prefix (e.g., tenant1_orders, tenant2_orders). The application uses DynamoDB Streams to replicate data to a central analytics table. Recently, the company added a new large tenant that generates 10x more write traffic than any other tenant. The DynamoDB Streams for the large tenant's table is falling behind by several hours, causing stale data in the analytics table. The company has already increased the write capacity of the large tenant's table to 50,000 WCUs, but the streams lag persists. The analytics table is also in DynamoDB and uses a Global Secondary Index (GSI) for querying. The streams processing Lambda function performs simple transformations and writes to the analytics table. The Lambda function is not throttled. Which action would resolve the streams lag?
⚠ Common exam trap
Many exam-takers assume increasing write capacity alone will resolve stream lag, but they overlook that stream shard count is tied to physical partitions, which only increase with on-demand mode or by triggering partition splits through sustained high throughput.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable DynamoDB on-demand mode for the large tenant's table to allow automatic scaling of stream shards.
DynamoDB Streams shards are directly tied to the physical partitions of the table. When a table is in provisioned mode, the number of stream shards is fixed and determined by the table's partitions, which cannot scale independently. Enabling on-demand mode allows DynamoDB to automatically split partitions and thus increase the number of stream shards, enabling higher stream throughput to keep up with the large tenant's write volume. This directly addresses the root cause of the streams lag without requiring manual partition management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable DynamoDB on-demand mode for the large tenant's table to allow automatic scaling of stream shards.
Why this is correct
On-demand mode adjusts the number of stream shards based on write traffic, which can help with lag.
- ✗
Remove the GSI from the analytics table to reduce write amplification.
Why it's wrong here
The GSI on the analytics table does not affect the stream shards of the source table.
- ✗
Increase the Lambda function's reserved concurrency to the maximum.
Why it's wrong here
The Lambda function is not throttled, so increasing concurrency will not help.
- ✗
Increase the write capacity of the large tenant's table to 100,000 WCUs.
Why it's wrong here
Increasing WCUs does not increase the number of stream shards; the bottleneck is at the shard level.
Visual reference
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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