Quick Answer
The answer is AWS IoT Core, as it is purpose-built to ingest IoT streaming data to S3 for near-real-time analytics. This service excels at handling high-throughput, small-message payloads—each event under 1 KB and totaling about 5 GB per hour—by supporting MQTT, HTTP, and WebSocket protocols, and it integrates directly with Amazon S3 through IoT Core rules, eliminating the need for intermediate processing. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your ability to match the service to the workload’s specific constraints: high device count, low-latency ingestion, and small message size. A common trap is choosing Amazon Kinesis Data Streams, which is better for larger, variable-sized records or complex stream processing, not for lightweight IoT telemetry. Remember the memory tip: “IoT Core for small, frequent messages to S3; Kinesis for larger, heavier streams.”
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
AWS IoT Core
Designed for IoT device ingestion.
AWS DataSync
Why wrong: For large data transfers between storage systems.
Amazon AppFlow
Why wrong: For SaaS data ingestion.
Amazon Kinesis Data Streams
Why wrong: Not the direct IoT ingestion service.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Designed for IoT device ingestion.
Related concept
Read the scenario before looking for a memorised answer.
AWS DataSync
Why it's wrong here
For large data transfers between storage systems.
Amazon AppFlow
Why it's wrong here
For SaaS data ingestion.
Amazon Kinesis Data Streams
Why it's wrong here
Not the direct IoT ingestion service.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
AWS IoT Core uses MQTT (Message Queuing Telemetry Transport), a lightweight publish/subscribe protocol optimized for constrained devices and low-bandwidth networks, with QoS levels (0, 1) to balance reliability and overhead. The IoT Core Rules Engine can route messages to S3, Lambda, or Kinesis without custom code, and it supports device shadows and topic-based filtering, which are essential for managing large IoT fleets. In a real-world scenario, a fleet of 10,000 sensors sending 100-byte messages every 10 seconds would generate ~360 MB/hour, well within IoT Core's free tier limits for the first 250 million messages per month.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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