DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is designing a data ingestion pipeline to load JSON files from Amazon S3 into Amazon Redshift. Which TWO methods can be used to load the data efficiently?
⚠ Common exam trap
Candidates often confuse Amazon Kinesis Data Firehose's ability to 'deliver' to Redshift with the actual loading mechanism, not realizing that Firehose only writes to S3 and then triggers a COPY command, making Option A a distractor for a direct load method.
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 the Redshift COPY command to load from S3.
The Redshift COPY command is specifically designed to efficiently load large datasets from Amazon S3 by automatically parallelizing the data across cluster nodes, leveraging the cluster's compute resources for high-throughput ingestion. It supports JSON data natively via the 'json' option, making it ideal for loading JSON files directly from S3 without intermediate transformations.
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 Amazon Kinesis Data Firehose to directly load into Redshift.
Why it's wrong here
Firehose delivers to Redshift via S3, but the question asks for methods; this is indirect but acceptable? However, Firehose can load directly into Redshift, but it's not as efficient as COPY for batch loads. I'll mark it wrong because the question asks for 'efficiently' and COPY is preferred.
- ✗
Use AWS DMS to replicate from S3 to Redshift.
Why it's wrong here
DMS is for database migrations, not S3 to Redshift bulk loads.
- ✓
Use the Redshift COPY command to load from S3.
Why this is correct
COPY is the fastest way to bulk load from S3.
- ✓
Use a staging table in S3 and then COPY into Redshift.
Why this is correct
This is common: COPY from S3 to staging, then INSERT into target.
- ✗
Use individual INSERT statements in a loop.
Why it's wrong here
INSERT is slow for large datasets.
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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Senior Network & Security Engineer · founder of Courseiva
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