MLS-C01 Data Engineering Practice Question
A company uses Amazon Redshift for its data warehouse. The data engineering team needs to load 10 TB of data from Amazon S3 into Redshift every night. The team wants to minimize the load time and use the fewest number of COPY commands. The data is in CSV format and is partitioned by date in S3. Which approach should the team take?
⚠ Common exam trap
Watch out — candidates often assume multiple COPY commands (one per partition) are needed for partitioned data, but Redshift's manifest file allows a single COPY command to load from many S3 objects in parallel, which is faster and simpler.
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 a manifest file with a single COPY command.
Using a manifest file with a single COPY command is the most efficient approach because it allows Redshift to load data from multiple S3 objects (partitioned by date) in parallel, automatically splitting the workload across cluster nodes. This minimizes load time by leveraging Redshift's parallel processing without requiring multiple COPY commands or manual concatenation, and it avoids the overhead of additional services like AWS Glue for a straightforward bulk load.
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 a manifest file with a single COPY command.
Why this is correct
A manifest file allows Redshift to load from multiple files in parallel efficiently.
- ✗
Use multiple COPY commands, one per partition.
Why it's wrong here
Multiple COPY commands add overhead and are slower than a single optimized COPY.
- ✗
Concatenate all data into a single large file before loading.
Why it's wrong here
A single 10 TB file is impractical; Redshift performs best with many files.
- ✗
Use AWS Glue to transform the data and then load into Redshift.
Why it's wrong here
Introducing AWS Glue adds an extra ETL transformation step that increases total load time, directly contradicting the requirement to minimise load time, whereas Redshift’s native COPY command can load CSV data partitioned by date in S3 in a single parallelised operation. This option is tempting because Glue is designed for complex data preparation and schema conversion tasks, and would be correct if the data required cleansing or format changes before ingestion.
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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