MLS-C01 Data Engineering Practice Question
A company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?
⚠ Common exam trap
Many candidates confuse AWS Lambda's event-driven nature with true stream processing, overlooking its concurrency and latency limitations for high-throughput aggregation, or they assume Spark Streaming is always the best choice for real-time without considering Flink's superior sub-second latency and fully managed nature on Kinesis Data Analytics.
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
✓
Amazon Kinesis Data Analytics for Apache Flink.
Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it provides a fully managed, serverless runtime for Apache Flink, which is designed for stateful stream processing at scale. It can aggregate millions of events per second with sub-second latency using exactly-once semantics and built-in checkpointing, meeting the near-real-time dashboard requirements without any infrastructure 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.
- ✓
Amazon Kinesis Data Analytics for Apache Flink.
Why this is correct
Kinesis Data Analytics with Flink provides low-latency, stateful stream processing at scale.
- ✗
AWS Lambda functions triggered by Kinesis Data Streams.
Why it's wrong here
Lambda has concurrency limits and may throttle at high throughput; also, it is not stateful for aggregations.
- ✗
Amazon EMR with Spark Streaming.
Why it's wrong here
EMR requires provisioning and tuning; it is not fully managed without additional effort.
- ✗
Amazon Redshift with materialized views refreshed frequently.
Why it's wrong here
Redshift is optimized for batch analytics; sub-second latency is not feasible with frequent refreshes.
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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