DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline for IoT sensor data. The data is generated at a high velocity and must be processed in near real-time. The pipeline must also handle bursty traffic. Which TWO AWS services should be combined to achieve this? (Choose TWO.)
⚠ Common exam trap
The DEA-C01 exam often tests the distinction between streaming services (Kinesis Data Streams) and batch/queue services (SQS, S3), so the trap here is assuming SQS can handle real-time streaming or that S3 can serve as a primary ingestion point for high-velocity data.
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 Analytics
Amazon Kinesis Data Streams is designed for real-time, high-velocity data ingestion, providing durable, ordered data streams that can handle bursty traffic by scaling shard capacity. Amazon Kinesis Data Analytics can process these streams in near real-time using SQL or Apache Flink, enabling immediate transformations and analytics without needing to store data first.
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 S3
Why it's wrong here
Amazon S3 is object storage for durable batch and archival data, not a high-velocity, near-real-time streaming ingestion service, so it cannot absorb bursty IoT traffic as the entry point. It is tempting because S3 commonly serves as the pipeline's final destination or data lake, which is a valid role, but not for real-time ingestion.
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics runs SQL or Apache Flink over streaming data, delivering the near real-time transformation the pipeline demands. Paired with a stream, it consumes bursty IoT input continuously, satisfying the high-velocity processing constraint without batch delays.
- ✗
Amazon Simple Queue Service (SQS)
Why it's wrong here
Amazon SQS is a pull-based message queue for decoupling application components, not a managed streaming service with shards, retention and replay for high-velocity IoT data. It is tempting because SQS does buffer bursty traffic, and it would be correct for decoupling microservices or buffering discrete application messages, not continuous sensor streams.
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AWS Glue
Why it's wrong here
AWS Glue is a serverless ETL and data catalogue service that runs batch or scheduled transformation jobs, so it cannot ingest continuous high-velocity sensor streams in near real-time. It is tempting because Glue is a standard AWS data-integration component, and it would be the right choice for scheduled batch transformation or cataloguing after ingestion.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Kinesis Data Streams ingests high-velocity IoT telemetry with shard-level scaling, absorbing bursty traffic by adding capacity. It provides the durable, ordered, near real-time transport layer that downstream analytics consumes, directly meeting the velocity and burst-handling constraints.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.