SAP-C02 Design for New Solutions Practice Question
A company is designing a serverless data processing pipeline. An AWS Lambda function processes records from an Amazon Kinesis Data Stream. The function runs for an average of 30 seconds per record, and the stream has 10 shards. The company expects a sustained load of 5,000 records per second. What is the primary consideration to ensure the Lambda function can scale to handle the load?
⚠ Common exam trap
The trap here is that candidates focus on Lambda's concurrency limits or timeouts, but the real bottleneck is the iterator age and the inability to process records faster than they arrive per shard, which is a fundamental scaling constraint in Kinesis-Lambda integrations.
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
✓
Ensure that the Lambda function processes each batch within the Kinesis stream's iterator age.
The primary consideration is to ensure that the Lambda function processes each batch within the Kinesis stream's iterator age (default 7 days). With 5,000 records/sec across 10 shards, each shard receives 500 records/sec. If each record takes 30 seconds to process, the Lambda function must process records faster than they arrive to avoid falling behind and exceeding the iterator age, which would cause data loss. The iterator age metric tracks how far behind the consumer is, and if it grows unbounded, records will expire before being processed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure that the Lambda function processes each batch within the Kinesis stream's iterator age.
Why this is correct
Each shard is processed by a single Lambda instance; if processing takes too long, the iterator age grows and records may expire.
- ✗
Request a service quota increase for Lambda concurrent executions.
Why it's wrong here
The default concurrent execution limit is high enough for 10 shards; the issue is not account-level limits.
- ✗
Set a reserved concurrency of 500 for the Lambda function.
Why it's wrong here
Reserved concurrency limits the function, but the bottleneck is the number of shards, not account concurrency limits.
- ✗
Increase the Lambda function timeout to more than 30 seconds.
Why it's wrong here
Timeout does not affect scaling; the issue is concurrency per shard.
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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