DEA-C01 Data Operations and Support Practice Question
A data engineer is designing a data pipeline that ingests streaming data from an IoT device fleet. The data must be processed in near real-time and stored in Amazon S3 for long-term analytics. Which TWO AWS services should the engineer use together to achieve this?
⚠ Common exam trap
DEA-C01 often tests whether candidates can distinguish ingestion services (Kinesis) from storage/query services (Athena, Glue) and decoupling services (SQS), so picking Athena or Glue for the ingestion leg is the common mistake.
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 Firehose
Amazon Kinesis Data Streams (D) is correct because it provides a highly scalable, low-latency ingestion service for real-time streaming data from thousands of IoT devices, allowing custom consumers to process records in near real-time. Amazon Kinesis Data Firehose (C) is correct because it can consume that stream (or receive data directly) and reliably deliver it in near real-time to Amazon S3 for long-term analytics, handling batching, compression, and format conversion. Together they form the canonical AWS pattern for real-time IoT ingestion plus durable S3 storage. Amazon Athena (A) is only a query service over S3 and does not ingest or process streaming data. AWS Glue (B) is a serverless ETL/catalog service, not a real-time streaming ingestion or delivery mechanism. Amazon SQS (E) is a message queue for decoupling applications, not designed for high-throughput real-time streaming ingestion into S3.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data already in S3 using SQL; it neither ingests nor processes streaming records, so it cannot form part of the ingestion path. It suits ad-hoc serverless analytics over stored datasets, not near real-time capture from an IoT fleet.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a batch-oriented extract, transform, and load (ETL) service, not a stream-processing engine; it lacks native support for continuous, low-latency ingestion from IoT device fleets, which requires a service like Amazon Kinesis Data Streams or Amazon Managed Streaming for Apache Kafka to handle unbounded data in near real-time. It is tempting because Glue can process and catalogue data stored in Amazon S3 for analytics, and would be the correct choice if the pipeline first buffered the streaming data into S3 via another service and then ran scheduled batch transformations on that landed data.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Kinesis Data Firehose provides a fully managed delivery stream that ingests streaming records and automatically batches, transforms and writes them into Amazon S3, satisfying the near real-time processing and long-term S3 storage requirements without custom consumer code.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Amazon Kinesis Data Streams ingests the IoT telemetry continuously with sub-second latency, satisfying the near real-time processing constraint. It durably buffers records across shards for 24 hours by default, letting consumers read and write the stream into Amazon S3 for long-term analytics without data loss.
- ✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
SQS is a decoupled message queue for asynchronous point-to-point delivery, not a streaming platform with shards, replay or consumer offsets; it cannot feed near real-time processing at fleet scale. It suits buffering between microservices, not continuous IoT stream ingestion.
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
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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.