DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to ingest data from an Amazon S3 bucket into Amazon Redshift for analytics. The data is in CSV format and the Redshift table already exists. Which service can be used to perform this ingestion with minimal configuration?
⚠ Common exam trap
The trap here is that candidates often overthink and choose AWS Glue or Kinesis Firehose because they assume a managed service is always required, but the COPY command is the simplest and most efficient native tool for batch loading from S3 into an existing Redshift table.
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 Redshift COPY command
The Amazon Redshift COPY command is the most direct and minimal-configuration method to load data from an S3 bucket into an existing Redshift table. It is purpose-built for bulk data ingestion from S3, supports CSV format natively, and requires only the table name, S3 path, IAM role, and format options — no additional services or pipelines needed.
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 it's wrong here
AWS Glue requires writing or generating an ETL script to transform and load data, whereas the correct service—Amazon Redshift’s COPY command—ingests CSV from S3 directly into an existing table with no code. It is tempting because Glue is a common choice for S3-to-Redshift pipelines when schema discovery or complex transformations are needed, but the stem specifies minimal configuration and an already-existing table, making Glue’s extra orchestration overhead unnecessary.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose delivers streaming data to destinations like S3, Redshift via S3, or OpenSearch, but it cannot read an existing S3 bucket as a source; it only ingests from producers such as Kinesis Data Streams or direct PUT. It would be correct for streaming ingestion into Redshift, not bulk loading CSV files already in S3.
- ✓
Amazon Redshift COPY command
Why this is correct
The COPY command loads CSV files directly from Amazon S3 into an existing Redshift table using parallel processing, requiring only an IAM role for authorisation. It satisfies the minimal-configuration constraint because no external ETL service, cluster provisioning, or intermediate staging is needed.
- ✗
AWS Database Migration Service (DMS)
Why it's wrong here
DMS replicates data between databases and requires source and target endpoints, replication instances and task configuration, so it does not meet the minimal-configuration requirement for CSV files already in S3. It is tempting for database migration, but the COPY command is the direct load mechanism.
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
1 more way this is tested on DEA-C01
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Variation 1. A data engineer needs to ingest data from an Amazon S3 bucket into an Amazon Redshift table on a daily schedule. The data is in CSV format and the schema matches. Which service is simplest for this batch ingestion?
easy- ✓ A.Amazon Redshift COPY command
- B.AWS Glue ETL job with JDBC connection
- C.AWS Data Pipeline
- D.Amazon Athena CREATE TABLE AS SELECT
Why A: The Amazon Redshift COPY command is the simplest and most efficient method for batch loading data from Amazon S3 into Redshift when the schema matches and the data is in CSV format. It leverages Redshift's massively parallel processing (MPP) architecture to read data directly from S3, automatically handling compression, encryption, and error logging without requiring any intermediate services or custom code.
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.