DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer needs to ingest streaming data from an IoT fleet into Amazon S3 for near-real-time analytics. The data volume is approximately 5 GB per hour, and each event is less than 1 KB. Which AWS service should be used as the ingestion endpoint?

⚠ Common exam trap

It's easy for candidates to default to Amazon Kinesis Data Streams for any streaming workload, overlooking that AWS IoT Core is the specialized, fully managed service designed specifically for IoT device ingestion, with native MQTT support and direct S3 integration via rules.

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

✓

AWS IoT Core

AWS IoT Core is purpose-built for ingesting data from IoT devices, supporting MQTT, HTTP, and WebSocket protocols. It can handle millions of devices and high-throughput, small-message payloads (each event <1 KB) and integrates directly with Amazon S3 via IoT Core rules, making it the ideal ingestion endpoint for near-real-time analytics on streaming IoT data.

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 IoT Core
Why this is correct
AWS IoT Core ingests high-volume, small-payload device telemetry and can route it directly to Amazon S3 via rules, meeting the 5 GB per hour near-real-time requirement. Its native MQTT support suits sub-1 KB events from a fleet, unlike services designed for batch or large-object transfer.
✗
AWS DataSync
Why it's wrong here
DataSync performs scheduled or on-demand bulk file transfers between on-premises storage, edge locations, and AWS, so it cannot serve as a continuously listening endpoint for sub-kilobyte IoT events. It is tempting because it lands data in Amazon S3 efficiently, and would be correct for migrating large file shares or replicating datasets, not streaming telemetry.
✗
Amazon AppFlow
Why it's wrong here
AppFlow moves data between SaaS applications and AWS services on scheduled or event-driven flows, not as a persistent endpoint for thousands of small IoT device events. It is tempting because it writes to Amazon S3 without code, and would be correct for syncing Salesforce or Slack records rather than fleet telemetry.
✗
Amazon Kinesis Data Streams
Why it's wrong here
Kinesis Data Streams ingests and buffers records but does not itself deliver them into Amazon S3; a separate consumer such as Firehose or Lambda must read the stream and write objects. It is tempting because it handles high-throughput streaming, and would be correct when custom consumers need replay or multiple downstream readers.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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