MLS-C01 Data Engineering Practice Question
Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a real-time data ingestion pipeline? (Choose three.)
⚠ Common exam trap
Candidates often confuse Kinesis Data Firehose's built-in Lambda transformations with the custom real-time processing capabilities of Kinesis Data Streams, overlooking that Firehose does not allow direct consumer applications or sub-second latency.
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
✓
The need for custom real-time processing logic using consumer applications.
Kinesis Data Streams supports custom real-time processing via consumer applications using the Kinesis Client Library (KCL) or AWS Lambda, enabling fine-grained control over record processing, checkpointing, and custom logic. This is a key differentiator from Kinesis Data Firehose, which only supports built-in transformations via Lambda and does not allow direct consumer access to the stream.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The need for built-in data transformation and analytics.
Why it's wrong here
Incorrect: Neither has built-in analytics; Firehose can invoke Lambda for transformation.
- ✓
The need for custom real-time processing logic using consumer applications.
Why this is correct
Correct: Data Streams supports custom consumers; Firehose does not.
- ✓
The required end-to-end latency (seconds vs. minutes).
Why this is correct
Correct: Data Streams has sub-second latency; Firehose buffers data, introducing minutes of delay.
- ✓
The need to manually manage shard capacity and scaling.
Why this is correct
Correct: Data Streams requires manual shard management; Firehose auto-scales.
- ✗
The requirement for exactly-once delivery semantics.
Why it's wrong here
Incorrect: Neither service guarantees exactly-once delivery.
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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