MLS-C01 Data Engineering Practice Question
A company is designing a data pipeline to analyze customer behavior. The pipeline must handle real-time streaming data and batch data. The data must be stored in a data lake on Amazon S3 and also made available for interactive queries. Which THREE services should be combined to build this pipeline? (Choose THREE.)
⚠ Common exam trap
A common mix-up: candidates confuse Amazon Redshift as a query engine for S3 data, but Redshift requires data to be loaded into its cluster, whereas Athena queries data in place, making Athena the correct choice for interactive queries on the data lake.
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
Amazon Kinesis Data Streams is correct because it is the primary AWS service for ingesting and processing real-time streaming data at scale. It can capture and store streaming data from sources like clickstreams or IoT devices, making it available for downstream consumers such as AWS Glue or Amazon Athena for analysis.
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
Why this is correct
Real-time data ingestion.
- ✓
AWS Glue
Why this is correct
For ETL and cataloging.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is not a data lake; it's a data warehouse. Athena is better for direct S3 queries.
- ✗
Amazon DynamoDB Streams
Why it's wrong here
Not used for S3 data lake; it's for DynamoDB changes.
- ✓
Amazon Athena
Why this is correct
Interactive querying on S3.
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
Related to this question
About these practice questions
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.