DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to ingest streaming data from thousands of IoT devices and immediately process each record with minimal latency. Which AWS service should be used as the ingestion point?
⚠ Common exam trap
DEA-C01 often tests the distinction between ingestion services (Kinesis, MSK, IoT Core) and processing services (Lambda, Glue, EMR), so candidates mistakenly pick Lambda as the 'streaming' answer.
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 Streams
Amazon Kinesis Data Streams is designed as a massively scalable, low-latency ingestion service for streaming data, capable of handling thousands of producers (IoT devices) and delivering records to consumers within milliseconds. It durably stores data in shards for up to 365 days and integrates natively with Lambda, Kinesis Data Analytics, and Firehose for immediate processing. This makes it the canonical ingestion point for real-time IoT pipelines on AWS.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
AWS Lambda is a compute service that processes events after they arrive; it does not provide the durable, scalable ingestion endpoint thousands of IoT devices need to publish to. It is tempting because Lambda can be invoked by streams, but that is processing, not ingestion — Amazon Kinesis Data Streams is the ingestion point.
- ✗
Amazon S3
Why it's wrong here
Amazon S3 is object storage, not a low-latency streaming ingestion endpoint; devices would need another service to deliver records, and per-object writes add latency. It is tempting because S3 can receive IoT data via rules or Firehose, but that is delivery, not ingestion — Kinesis Data Streams accepts device writes directly.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Amazon Kinesis Data Streams ingests high-volume device telemetry with millisecond-level latency and preserves record order per shard, letting consumers process each record immediately. It satisfies the minimal-latency, per-record processing constraint, unlike Amazon S3 or Kinesis Data Firehose, which batch and buffer before delivery.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a serverless ETL and catalog service that runs batch or micro-batch jobs, introducing scheduling latency rather than per-record streaming ingestion. It is tempting because Glue supports streaming ETL jobs, but it consumes from a stream rather than acting as the endpoint devices publish to; Kinesis Data Streams is that ingestion point.
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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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.