DEA-C01 Data Ingestion and Transformation Practice Question
A company wants to ingest streaming data from thousands of IoT devices into Amazon S3 with minimal latency and then transform the data using Spark SQL. Which AWS service should be used for data ingestion?
⚠ Common exam trap
Test-takers frequently confuse data ingestion services (Kinesis Data Firehose) with data processing or query services (EMR, Glue, Athena), leading candidates to pick EMR for its Spark SQL capability instead of recognizing that Firehose handles the ingestion step before transformation.
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 is the correct choice because it is a fully managed service designed for ingesting streaming data into Amazon S3 with near-real-time latency (typically 60 seconds or less). It can directly write data to S3 without requiring custom code or additional infrastructure, and it supports optional transformations via AWS Lambda, making it ideal for the described use case of streaming IoT data ingestion.
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 processes and analyses data using frameworks such as Spark; it is not an ingestion service for device streams. It is tempting because the stem requires Spark SQL transformation, and EMR would be correct for that processing stage, but ingestion into S3 needs a dedicated streaming delivery service.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a serverless ETL and catalog service that runs batch or scheduled Spark jobs, not a low-latency streaming ingestion endpoint for thousands of devices. It is tempting because Glue performs the Spark SQL transformation named in the stem, but ingestion at minimal latency requires Amazon Kinesis Data Streams or Amazon MSK.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data already in Amazon S3 using SQL; it does not ingest streaming data from devices. It is tempting because the stem mentions SQL transformation, and Athena would be correct for ad-hoc querying of S3-resident data, but ingestion requires a service that writes streams into S3.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Kinesis Data Firehose ingests streaming data and delivers it directly into Amazon S3 with minimal latency, requiring no custom consumer code. It satisfies the ingestion requirement, after which Spark SQL can transform the landed data.
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.