DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to ingest streaming data from thousands of IoT devices into AWS for near-real-time analytics. The data volume varies significantly and can spike unpredictably. The engineer wants to minimize operational overhead and ensure that data is durably stored as soon as it arrives. Which AWS service combination should the engineer use?
⚠ Common exam trap
Many exam-takers confuse Kinesis Data Firehose (which batches and delivers to destinations like S3) with Kinesis Data Streams (which provides real-time, durable storage and processing), leading them to choose Option B despite its lack of true near-real-time ingestion and automatic scaling for spikes.
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
✓
Use Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling.
Amazon Kinesis Data Streams (KDS) is designed for ingesting large volumes of streaming data with automatic scaling (via shard splitting/merging) and provides durable storage (default 24-hour retention, extendable to 365 days) as soon as records are received. AWS Lambda can be subscribed to the stream to process records in near-real-time, scaling automatically based on the number of shards, which minimizes operational overhead and handles unpredictable spikes without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon S3 Transfer Acceleration with S3 Event Notifications to trigger AWS Lambda for processing.
Why it's wrong here
S3 Transfer Acceleration accelerates uploads to S3 but is not designed for streaming ingestion; S3 Event Notifications have latency and are not ideal for near-real-time.
- ✗
Use Amazon Kinesis Data Firehose to ingest data into Amazon S3 and use AWS Lambda to transform data during delivery.
Why it's wrong here
Kinesis Data Firehose buffers data before delivering to S3, which introduces latency and is not suitable for sub-second analytics.
- ✗
Use Amazon Simple Queue Service (SQS) to buffer the streaming data and configure an Auto Scaling group of EC2 instances to poll and process the data.
Why it's wrong here
SQS is a message queue, not a streaming platform; it does not support partitioning, replay, or ordered processing across shards.
- ✓
Use Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling.
Why this is correct
Kinesis Data Streams provides durable, scalable, low-latency ingestion; Lambda can process each shard in parallel and scales automatically.
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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JA
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.