DEA-C01 Data Ingestion and Transformation Practice Question
A company needs to transform JSON data from an S3 bucket into a structured format for Amazon Redshift. The transformation should be done serverlessly. Which service should be used?
⚠ Common exam trap
A common mix-up: candidates confuse Amazon Athena's serverless SQL querying capability with ETL transformation, but Athena cannot transform or write data into a different format for Redshift—it only reads and queries data in place.
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 the correct choice because it is a fully managed, serverless ETL service designed specifically for transforming and preparing data for analytics, including converting JSON to structured formats like Parquet or ORC for Amazon Redshift. It can crawl the S3 source, infer schemas, and run Spark-based transformation jobs without provisioning any infrastructure, aligning perfectly with the serverless requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue
Why this is correct
Glue provides serverless ETL capabilities.
- ✗
Amazon EMR
Why it's wrong here
EMR is not serverless; it requires cluster management.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying, not transforming.
- ✗
AWS Lambda
Why it's wrong here
Lambda is not designed for large-scale ETL transformations.
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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Same concept, more angles
4 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. A data engineer runs this CLI command on an S3 bucket. The data is ingested from multiple sources. Which AWS service would be best to process these files in a single batch transformation?
easy- A.AWS Lambda
- B.Amazon Kinesis Data Analytics
- C.Amazon Athena
- ✓ D.AWS Glue
Why D: AWS Glue is designed for batch ETL processing, capable of handling multiple files of varying sizes in a single transformation job. Option A is wrong because Lambda has limitations on execution time (15 minutes) and memory (10 GB), making it unsuitable for large-scale batch transforms. Option B is wrong because Kinesis Data Analytics is for real-time stream processing, not batch. Option C is wrong because Athena is an interactive query service for ad-hoc analysis of data in S3, not for batch transformations.
Variation 2. A company has CSV files in an S3 bucket that need to be converted to Parquet and loaded into a Redshift table daily. The transformation is a simple schema mapping without joins. Which AWS Glue feature is BEST suited for this task?
easy- ✓ A.AWS Glue ETL job
- B.AWS Glue DataBrew
- C.AWS Glue Workflow
- D.AWS Glue Crawler
Why A: (AWS Glue ETL job) is the best suited because it can convert CSV to Parquet and load into Redshift daily. Option B (DataBrew) is a visual data preparation tool, not ideal for automated daily jobs. Option C (Workflow) orchestrates multiple jobs but does not perform transformation. Option D (Crawler) only discovers schema and catalogs data, not transform.
Variation 3. 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?
easy- A.Use AWS Lambda to transform the data on the fly and write to Redshift.
- B.Use Amazon EMR with Apache Spark to transform the data and load it into Redshift.
- ✓ C.Use AWS Glue to create an ETL job that runs on a schedule or trigger.
- D.Use Amazon Kinesis Data Firehose to transform and load data into Redshift in real time.
Why C: 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.
Variation 4. A data engineer needs to transform JSON data from an S3 bucket into Parquet format for efficient querying with Amazon Athena. The transformation must be serverless and event-driven. Which approach meets these requirements?
easy- A.Use AWS Glue with a scheduled crawler to convert the data.
- B.Use Amazon Athena to convert JSON to Parquet on the fly.
- ✓ C.Use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data.
- D.Use Amazon EMR with a long-running cluster to process S3 data.
Why C: S3 event notifications can trigger a Lambda function that launches an AWS Glue ETL job. Glue ETL jobs run PySpark code natively to convert JSON to Parquet, satisfying the serverless and event-driven requirements. Athena only queries existing data, Glue crawlers do not perform transformations, and EMR clusters are not serverless.
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.