DEA-C01 Data Ingestion and Transformation Practice Question
A company is building a data lake on Amazon S3. Data arrives from multiple sources in JSON, CSV, and Avro formats. The data must be transformed to Parquet and partitioned by date and source. Which TWO services can perform this transformation with minimal custom code? (Choose TWO.)
⚠ Common exam trap
Many candidates confuse AWS Lake Formation's data catalog and permission features with actual data transformation capabilities, or they assume Kinesis Data Firehose can transform existing S3 objects when it only processes streaming data in transit.
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 EMR with Spark
Both Amazon EMR with Spark and AWS Glue ETL jobs can perform the transformation with minimal custom code. Spark natively supports reading JSON, CSV, and Avro formats and writing Parquet with partitioning by date and source, requiring only a concise PySpark or Scala script. Similarly, AWS Glue provides a managed Spark environment with built-in transforms and crawlers, allowing users to write Spark scripts or use visual ETL jobs with minimal code. The other options either lack native transformation capabilities (Lake Formation, Athena CTAS queries) or are designed for streaming data (Kinesis Data Firehose).
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 Spark
Why this is correct
EMR can run Spark for large-scale transformations.
- ✗
AWS Lake Formation
Why it's wrong here
Lake Formation is for data lake management, not transformation.
- ✗
Amazon Athena CTAS queries
Why it's wrong here
Athena is for querying, not transformation pipelines.
- ✓
AWS Glue ETL jobs
Why this is correct
Glue provides built-in transforms and can write Parquet.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose is for streaming, not batch transformation.
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 1,711 original DEA-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 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.