DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform JSON data from an S3 bucket into Parquet format and load it into Amazon Redshift. The transformation must be performed incrementally as new data arrives. 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
✓
Use AWS Glue to create an ETL job that runs on a schedule or trigger.
AWS Glue provides a serverless ETL service that can run jobs triggered by S3 events to transform data incrementally and load into Redshift. Option A (AWS Lambda) can be used for simple transformations but may hit time limits for complex transformations. Option B (Amazon EMR) is more suited for large-scale big data processing but requires cluster management. Option D (Amazon Kinesis Data Firehose) is for streaming data, not for batch transformation of existing S3 objects.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Lambda to transform the data on the fly and write to Redshift.
Why it's wrong here
Lambda transforms individual records but cannot natively write Parquet to Redshift or manage incremental S3-to-Redshift loads at scale. It is tempting because Lambda responds to S3 events, yet the required JSON-to-Parquet conversion and incremental loading is handled by AWS Glue jobs with the Redshift connector.
- ✗
Use Amazon EMR with Apache Spark to transform the data and load it into Redshift.
Why it's wrong here
EMR with Spark provisions a cluster, incurring cost and startup latency even when idle, so it cannot process each arriving object incrementally without persistent infrastructure. It suits large scheduled batch transformations or complex Spark workloads, not the lightweight event-driven incremental conversion the scenario demands.
- ✓
Use AWS Glue to create an ETL job that runs on a schedule or trigger.
Why this is correct
AWS Glue provides managed, serverless Spark ETL with job bookmarks, enabling incremental processing of only new S3 objects. Its native Redshift connector and Parquet conversion satisfy the transformation and load requirements, while schedule or event triggers handle the incremental arrival constraint.
- ✗
Use Amazon Kinesis Data Firehose to transform and load data into Redshift in real time.
Why it's wrong here
Kinesis Data Firehose buffers records for seconds before delivery, so it cannot perform the incremental JSON-to-Parquet transformation on arrival from S3. It is tempting because Firehose converts formats and loads Redshift, but that suits streaming sources, not S3 objects needing event-driven incremental processing via AWS Glue.
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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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.