DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a streaming ingestion pipeline using Amazon Kinesis Data Streams. The stream has 10 shards, and the data volume is expected to grow by 50% over the next month. The engineer needs to ensure that the pipeline can scale without manual intervention. Which approach should be used?
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 Kinesis stream to on-demand capacity mode
Kinesis Data Streams on-demand capacity mode automatically scales the number of shards based on the incoming traffic pattern, eliminating the need for manual intervention. Option A is incorrect because CloudWatch Alarms can trigger a Lambda function to add shards via the UpdateShardCount API, but this approach requires custom code and does not provide automatic scaling without manual setup. Option B is incorrect because Auto Scaling groups are used for EC2 instances, not for Kinesis stream shards. Option D is incorrect because Lambda functions can be used to scale shards programmatically, but this still requires custom implementation and is not a native automatic scaling feature.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set up a CloudWatch Alarm to trigger a Lambda function to add shards
Why it's wrong here
Setting up a CloudWatch Alarm to trigger a Lambda function to add shards is a custom solution, not a native auto-scaling feature of Amazon Kinesis Data Streams. Kinesis Data Streams does not offer built-in auto-scaling based on resource utilisation, meaning this approach would require complex custom logic within the Lambda to monitor metrics and call `UpdateShardCount` effectively. This option is tempting because CloudWatch Alarms and Lambda are fundamental for implementing custom automation, and this pattern is used for auto-scaling other AWS services like EC2 or DynamoDB.
- ✗
Use an Auto Scaling group to add more shards
Why it's wrong here
Auto Scaling groups are not applicable to Kinesis shards.
- ✓
Switch the Kinesis stream to on-demand capacity mode
Why this is correct
On-demand mode automatically scales shards based on ingestion throughput.
- ✗
Configure the stream to use a Lambda function that scales shards
Why it's wrong here
Lambda does not directly scale shards; it can be used with API but not natively.
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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