MLS-C01 Data Engineering Practice Question
A data engineer is building a data pipeline that ingests streaming data from IoT devices. The data must be processed in near real-time and stored in Amazon S3 for further analysis. Which AWS service should be used to capture and process the streaming data before storing it in S3?
⚠ Common exam trap
Many exam-takers confuse batch ETL services (like AWS Glue) or message queues (like SQS) with purpose-built streaming ingestion services, failing to recognize that Kinesis Data Streams and Firehose are the only AWS-native combination that captures, buffers, and delivers streaming data to S3 in near real-time without custom code.
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 capture the data and Amazon Kinesis Data Firehose to deliver it to S3.
Amazon Kinesis Data Streams is designed for real-time data ingestion and can capture streaming data from IoT devices with low latency. Amazon Kinesis Data Firehose then reliably loads that streaming data into Amazon S3, handling buffering, compression, and partitioning automatically. This combination provides the near real-time processing and durable storage required for the pipeline.
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 with S3 Event Notifications to trigger AWS Lambda for processing.
Why it's wrong here
S3 is object storage, not designed for real-time streaming ingestion.
- ✗
Use AWS Glue to perform ETL on the streaming data.
Why it's wrong here
AWS Glue is for batch ETL and data catalog, not real-time streaming.
- ✓
Use Amazon Kinesis Data Streams to capture the data and Amazon Kinesis Data Firehose to deliver it to S3.
Why this is correct
Kinesis Data Streams ingests real-time data and Kinesis Data Firehose delivers it to S3.
- ✗
Use Amazon Simple Queue Service (SQS) to buffer the data and then process it with AWS Lambda.
Why it's wrong here
SQS is a message queue; not optimized for streaming analytics.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.