MLS-C01 Data Engineering Practice Question
A company uses Amazon Redshift for data warehousing. The data engineering team needs to load data from multiple S3 buckets into Redshift daily. Each bucket contains files in different formats (CSV, JSON, Parquet). Which AWS service is BEST suited to automate this ingestion process?
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
✓
AWS Glue
AWS Glue is a fully managed ETL service that can crawl S3 buckets to discover schema, handle various formats (CSV, JSON, Parquet), and load data into Redshift. It automates the ingestion process without the need for manual infrastructure management. Amazon EMR with Spark requires more setup and management, AWS Data Pipeline is less flexible and older, and AWS Database Migration Service is designed for migrating entire databases, not for loading from S3.
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 with Apache Spark
Why it's wrong here
While Amazon EMR with Apache Spark provides a distributed processing framework capable of reading CSV, JSON, and Parquet from S3, it requires writing and maintaining custom transformation and load code, whereas the scenario demands a fully managed, schedule-driven ingestion pipeline that directly loads into Redshift without an intermediate compute cluster. The temptation arises because Spark excels at large-scale, complex ETL jobs across varied formats, and would be the correct choice if the task required heavy data transformation or joining datasets before loading.
- ✗
AWS Data Pipeline
Why it's wrong here
Data Pipeline is a legacy service; Glue is the modern recommended service.
- ✗
AWS Database Migration Service (DMS)
Why it's wrong here
DMS is for migrating databases, not for loading files from S3.
- ✓
AWS Glue
Why this is correct
Glue provides crawlers for schema discovery and ETL jobs for loading into Redshift.
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.