DEA-C01 Data Ingestion and Transformation Practice Question
A company needs to transform JSON data from an Amazon S3 bucket into Parquet format and load it into an Amazon Redshift cluster. The transformation includes joining with a reference table stored in Amazon RDS. Which AWS service is BEST suited for this task?
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 ETL job
(AWS Glue ETL job) is the best choice because it natively integrates with S3, RDS, and Redshift. Glue can read JSON from S3, connect to RDS via JDBC to join with the reference table, transform the data to Parquet using its built-in converter, and write directly to Redshift. Option A (AWS Data Pipeline) is older and less integrated for this purpose. Option C (Amazon Athena) can query S3 and convert to Parquet but cannot natively join with RDS without additional services. Option D (Amazon EMR with Spark) is possible but requires more setup and maintenance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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AWS Data Pipeline
Why it's wrong here
AWS Data Pipeline orchestrates scheduled data movement between AWS services but does not perform the JSON-to-Parquet transformation or RDS joins itself. It is tempting because it is correct for scheduling and dependency-driven ETL workflows between supported sources and destinations.
- ✓
AWS Glue ETL job
Why this is correct
AWS Glue ETL jobs run Apache Spark, so they can read JSON from S3, join it against the RDS reference table via a JDBC connection, and write Parquet into Redshift. This satisfies the transformation and cross-source join requirement in one managed job.
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Amazon Athena
Why it's wrong here
Athena queries data in S3 but cannot directly connect to RDS for joins in a single query.
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Amazon EMR with Spark
Why it's wrong here
Amazon EMR with Spark is a powerful platform for big data processing, making it tempting for complex transformations and custom code execution on petabyte-scale datasets. However, for this specific scenario, EMR introduces operational overhead by requiring cluster management, which is not optimal for a standard, recurring ETL pipeline from S3 to Redshift with an RDS join. A more serverless or fully managed ETL service would minimise administrative burden and align better with the 'BEST suited' criterion.
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 |
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JA
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.