Match each AWS service to its primary purpose in data engineering.
Drag a concept onto its matching description — or click a concept then click the description.
Serverless ETL and data catalog
Data warehousing and SQL analytics
Big data processing using Hadoop/Spark
Building and managing data lakes
Real-time streaming data ingestion
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon S3: Object storage for data lakes
The correct matches are: Amazon S3 → object storage for data lakes (A), AWS Glue → serverless ETL (B), Amazon Athena → interactive SQL queries on S3 (C), and Amazon Redshift → data warehousing (D). Common confusions involve mixing up services like Kinesis (streaming) with Glue (ETL) or Data Pipeline (orchestration) with Kinesis (ingestion).
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon S3: Object storage for data lakes
Why this is correct
Amazon S3 provides scalable object storage, ideal for building data lakes.
AWS Glue: Serverless data integration and ETL service
Why this is correct
AWS Glue is a serverless service for preparing and transforming data for analytics.
Amazon Athena: Interactive query service for data in S3 using SQL
Why this is correct
Amazon Athena allows querying data directly in S3 using standard SQL, without needing a separate data store.
Amazon Redshift: Petabyte-scale data warehousing
Why this is correct
Amazon Redshift is a fully managed data warehouse designed for large-scale analytics.
AWS Kinesis: Serverless ETL service
Why it's wrong here
Incorrect — AWS Kinesis is for real-time streaming data processing, not ETL. AWS Glue is the serverless ETL service.
AWS Data Pipeline: Real-time data ingestion
Why it's wrong here
Incorrect — AWS Data Pipeline orchestrates and automates data workflows, not real-time ingestion. AWS Kinesis handles real-time data ingestion.
Quick reference
| 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 |
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About these practice questions
This DEA-C01 question is part of Courseiva's 1,711-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.