DEA-C01 Data Operations and Support Practice Question
A data engineer is setting up a data pipeline to ingest streaming data from an IoT fleet. The data must be processed in near real-time and stored in Amazon S3 for analytics. Which THREE AWS services should the engineer consider using?
⚠ Common exam trap
DEA-C01 often tests the distinction between real-time streaming ingestion (Kinesis Data Streams) and near-real-time delivery to S3 (Kinesis Data Firehose), causing candidates to overlook Lambda as the processing layer or incorrectly select batch-oriented services like EMR or Glue.
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 Firehose (B) is correct because it is a fully managed service designed to reliably load streaming data directly into Amazon S3 (and other destinations) with near real-time delivery, requiring no server management. Amazon Kinesis Data Streams (E) is correct because it ingests and buffers high-throughput streaming data from IoT fleets in real time, allowing custom consumers to process the data before it is stored in S3. AWS Lambda (C) is correct because it can be invoked by Kinesis to process streaming records in near real-time, enabling serverless transformation or enrichment of the IoT data before it lands in S3. Amazon EMR (A) is not the best fit here because it is a batch-oriented big data processing platform (Hadoop/Spark) rather than a streaming ingestion service, and AWS Glue (D) is primarily a serverless ETL and data catalog service for batch and some streaming jobs, not a dedicated real-time ingestion pipeline component for this scenario.
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 EMR
Why it's wrong here
Amazon EMR runs batch MapReduce and Spark jobs on provisioned clusters, not continuous stream ingestion. It is tempting because EMR can process S3-resident data at scale, and would be the right choice for periodic batch analytics over historical IoT datasets rather than near real-time streaming.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Amazon Kinesis Data Firehose satisfies the near real-time ingestion and S3 delivery constraints by buffering streaming records and writing them directly to Amazon S3 without custom consumer code. It handles scaling, batching and format conversion automatically, so IoT telemetry lands in S3 for analytics with minimal operational overhead.
- ✓
AWS Lambda
Why this is correct
Lambda processes streaming records in near real-time without managing servers, invoked by Kinesis or via event source mappings. It transforms or enriches each batch before writing to S3, satisfying the low-latency processing requirement between ingestion and storage.
- ✗
AWS Glue
Why it's wrong here
Glue is a batch ETL service.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Kinesis Data Streams ingests the IoT telemetry continuously and durably, buffering records until consumers read them. This satisfies the near real-time processing constraint, since shards deliver records within milliseconds and can fan out to analytics consumers writing into Amazon S3.
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
Related to this question
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Same concept, more angles
1 more way this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Amazon Athena
- B.AWS Glue
- ✓ C.Amazon Kinesis Data Firehose
- ✓ D.Amazon Kinesis Data Streams
- E.Amazon Simple Queue Service (SQS)
Why C: 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.
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.