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 3.)
⚠ Common exam trap
A common misconception is that Kinesis Data Firehose supports custom real-time processing like Streams, but Firehose only allows optional Lambda transformations with limited control and no data replay capability.
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
✓
Need for custom data processing using AWS Lambda
Kinesis Data Streams supports custom processing via AWS Lambda consumers (using the Kinesis Client Library or direct integration), enabling real-time transformations, filtering, or enrichment. Kinesis Data Firehose does not natively support custom Lambda processing for transformation; it only allows optional Lambda functions for data format conversion or transformation before delivery, but not for arbitrary real-time processing logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ability to compress data before delivery
Why it's wrong here
Both can compress data before delivery.
- ✗
Ability to encrypt data at rest
Why it's wrong here
Both services support encryption at rest.
- ✓
Need for custom data processing using AWS Lambda
Why this is correct
Kinesis Data Streams supports custom processing with Lambda, Firehose has limited transformation.
- ✓
Data retention requirements
Why this is correct
Kinesis Data Streams retains data up to 365 days, Firehose does not retain data.
- ✓
Latency requirements for data delivery to S3
Why this is correct
Kinesis Data Firehose delivers data within 60 seconds, while Kinesis Data Streams requires custom consumer.
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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