- A
Enable DynamoDB on-demand mode for the large tenant's table to allow automatic scaling of stream shards.
On-demand mode adjusts the number of stream shards based on write traffic, which can help with lag.
- B
Remove the GSI from the analytics table to reduce write amplification.
Why wrong: The GSI on the analytics table does not affect the stream shards of the source table.
- C
Increase the Lambda function's reserved concurrency to the maximum.
Why wrong: The Lambda function is not throttled, so increasing concurrency will not help.
- D
Increase the write capacity of the large tenant's table to 100,000 WCUs.
Why wrong: Increasing WCUs does not increase the number of stream shards; the bottleneck is at the shard level.
Question 1,694 of 1,730
DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
DynamoDB Streams shards are mapped one-to-one with table partitions, and each shard can process up to 1 MB of data per second. For a table in provisioned mode, partition count is calculated as (WCUs / 3000 + RCUs / 6000), and partitions only split when throughput exceeds that calculation. On-demand mode automatically splits partitions as write traffic increases, thereby increasing stream shard count and allowing higher stream throughput. In real-world scenarios, a sudden spike from a large tenant can cause streams lag even if the source table has sufficient write capacity, because the fixed number of stream shards becomes a bottleneck.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable DynamoDB on-demand mode for the large tenant's table to allow automatic scaling of stream shards. — Option A is correct because 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.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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