MLS-C01 Data Engineering Practice Question
A data science team needs to process streaming data from thousands of IoT devices and perform real-time anomaly detection. The data must be persisted in Amazon S3 for batch processing later. Which combination of AWS services should be used to meet these requirements?
⚠ Common exam trap
Watch out — candidates often confuse AWS Glue's batch processing capabilities with real-time streaming analytics, or assume Lambda can handle continuous high-throughput ingestion without considering its timeout and scaling limitations.
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 Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.
Amazon Kinesis Data Streams provides durable, real-time ingestion for high-throughput IoT data. Kinesis Data Analytics can perform SQL-based anomaly detection on the stream, and Kinesis Data Firehose reliably delivers the processed or raw data to Amazon S3 for batch processing. This combination meets all requirements for streaming ingestion, real-time analytics, and persistent storage.
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 Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.
Why this is correct
This combination provides real-time ingestion, analytics, and durable storage.
- ✗
Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.
Why it's wrong here
Glue is a batch ETL service, not suitable for real-time anomaly detection.
- ✗
AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.
Why it's wrong here
Lambda cannot handle continuous high-throughput streaming and does not persist data to S3 natively.
- ✗
Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.
Why it's wrong here
SQS is not designed for high-throughput streaming and lacks real-time analytics capabilities.
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 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.