SAP-C02 Continuous Improvement for Existing Solutions Practice Question
A company is using an AWS Lambda function to process records from an Amazon Kinesis stream. The function stores results in an Amazon DynamoDB table. The team notices that the Lambda function sometimes fails due to throttling from DynamoDB. Which TWO actions should the team take to improve the continuous processing of records? (Choose TWO.)
⚠ Common exam trap
Watch out — candidates often choose to increase Lambda concurrency or shards, thinking more parallelism will improve processing, but they fail to recognize that the bottleneck is DynamoDB capacity, and increasing Lambda concurrency only worsens the throttling.
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
✓
Switch the DynamoDB table to on-demand capacity mode.
Switching the DynamoDB table to on-demand capacity mode eliminates the need to provision read/write capacity, allowing the table to automatically scale to handle any throttling caused by sudden spikes in Lambda writes. This directly addresses the throttling issue without requiring manual capacity 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.
- ✗
Increase the number of shards in the Kinesis stream to reduce data per Lambda invocation.
Why it's wrong here
This does not address DynamoDB throttling.
- ✗
Configure reserved concurrency for the Lambda function to limit its maximum concurrency.
Why it's wrong here
Reserved concurrency limits Lambda, not DynamoDB throttling.
- ✗
Increase the concurrency limit for the Lambda function to allow more parallel executions.
Why it's wrong here
More concurrency may increase throttling.
- ✓
Switch the DynamoDB table to on-demand capacity mode.
Why this is correct
On-demand mode eliminates throttling by scaling automatically.
- ✓
Enable DynamoDB auto scaling for the table to adjust read/write capacity automatically.
Why this is correct
Auto scaling adjusts capacity to handle load, reducing throttling.
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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