DEA-C01 Data Store Management Practice Question
A data engineer is configuring an Amazon Redshift cluster for a reporting workload. The team needs to load data from Amazon S3 into a Redshift table and wants the fastest possible load while keeping the data compressed. Which approach should the engineer use?
⚠ Common exam trap
The trap here is assuming that an orchestration service such as AWS Glue must be faster because it is serverless, when the actual load mechanism, COPY, determines throughput.
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 COPY command to load data from S3 into the Redshift table, specifying the appropriate compression and format options.
Amazon Redshift COPY is purpose-built for bulk ingestion from Amazon S3. It distributes the read across all compute slices in parallel, supports compressed and columnar formats, and applies options that validate and transform data during load. Alternatives such as row inserts, JDBC batching, or Spectrum materialization add layers that reduce throughput, so COPY remains the fastest supported method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run individual INSERT statements for each row from an AWS Lambda function that reads the S3 objects.
Why it's wrong here
Row-by-row INSERT statements are slow because each statement is a separate transaction that Redshift must parse, plan, and commit. Lambda execution time and concurrency limits further constrain throughput, and the approach adds significant cost. It cannot match the parallel bulk loading that COPY provides for large S3 datasets.
- ✗
Create an AWS Glue job that writes to Redshift using JDBC in small batches and commits after each batch.
Why it's wrong here
JDBC batch writes through Glue are far slower than the native COPY path because data travels through the job's driver and commits in small transactions. This creates overhead and can leave partial loads on failure. Glue can orchestrate a COPY, but using JDBC batches as the load mechanism defeats the goal of fastest load.
- ✗
Use Redshift Spectrum to create an external table over the S3 data and run CREATE TABLE AS SELECT to materialize it.
Why it's wrong here
Spectrum is designed for querying external data in place, and while CTAS can materialize results, it adds a query and write phase compared with a direct bulk load. For loading raw files into a native table, COPY is simpler and faster, and Spectrum would not apply the same load-time compression and format handling.
- ✓
Use the COPY command to load data from S3 into the Redshift table, specifying the appropriate compression and format options.
Why this is correct
COPY is Redshift's native parallel load utility. It reads from Amazon S3 using multiple slices in parallel, applies compression and format options such as GZIP, PARQUET, or ORC, and loads directly into the table. This is the recommended, most efficient path for bulk loads and avoids intermediate staging.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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