DEA-C01 Data Store Management Practice Question
A data engineer is building an Amazon Redshift data warehouse that ingests large staged files from Amazon S3 using the COPY command. The team wants to maximize load performance and minimize the time spent on ingestion. Which TWO practices should the engineer apply? (Choose two.)
⚠ Common exam trap
The trap here is equating query-performance tuning such as sort keys and VACUUM with load-performance tuning, when COPY speed is driven primarily by parallel file distribution and compressed columnar formats.
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
✓
Load compressed columnar files such as Parquet in a single COPY statement
COPY performance in Redshift depends on parallelism and data volume. Splitting input into multiple similarly sized files lets every slice read concurrently, and using compressed columnar formats such as Parquet reduces bytes moved and enables column pruning. Together these practices shorten load time. Sort keys, single-file loads, and post-load VACUUM operations affect query performance or add overhead rather than accelerating ingestion, so they do not belong in this optimization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Load compressed columnar files such as Parquet in a single COPY statement
Why this is correct
COPY supports columnar formats like Parquet and ORC, and columnar compression reduces the bytes transferred from S3 while allowing column-level pruning during load. Loading multiple Parquet files in one COPY statement lets Redshift distribute them across slices in parallel. Combining columnar compression with parallel file distribution reduces I/O and CPU work, directly improving ingestion throughput and shortening load windows.
- ✗
Run VACUUM SORT ONLY on the target table immediately after each COPY
Why it's wrong here
VACUUM SORT ONLY re-sorts rows to restore sort order after loads, which consumes cluster resources and time but does not improve the COPY operation itself. It addresses query performance degradation from unsorted data, not ingestion speed. Running it after every load adds overhead to the pipeline and competes with concurrent workloads. This practice does not help meet the stated goal of maximizing load performance.
- ✗
Use a single large compressed file to reduce the number of S3 GET requests
Why it's wrong here
A single large file is read by one slice, so it cannot exploit Redshift's parallel load architecture. The reduction in S3 GET requests does not compensate for the loss of parallelism, and load time scales with file size on one slice. Compression is beneficial, but it must be combined with multiple files. This option directly undermines the parallel-load goal, making it a poor choice.
- ✓
Split the input data into multiple files sized roughly equal and load them in parallel
Why this is correct
Redshift parallelizes COPY across slices, and each slice reads a subset of the input files. Using multiple files of similar size lets all slices work concurrently rather than having one large file processed by a single slice. This dramatically shortens load time for large datasets. A single monolithic file forces serial reads and underutilizes the cluster, so splitting into comparable files is a core performance practice for COPY.
- ✗
Add a sort key to the target table on the timestamp column before loading
Why it's wrong here
A sort key improves query performance by enabling zone maps and ordered scans, but it does not accelerate the COPY ingestion itself. In fact, loading unsorted data into a sorted table causes Redshift to sort during the load, which can add overhead. Defining a sort key is a query-optimization decision, not a load-throughput practice, so it does not satisfy the goal of minimizing ingestion time here.
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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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
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.