DEA-C01 Data Operations and Support Practice Question
A company is migrating its on-premises data warehouse to Amazon Redshift. The data includes tables with up to 100 columns and 500 million rows. The migration involves a full load followed by incremental updates. The company needs to minimize downtime during the final cutover. Which THREE strategies should the data engineer use to facilitate the migration? (Choose THREE.)
⚠ Common exam trap
DEA-C01 often tests whether candidates know that WLM queues and distribution keys are performance/concurrency constructs, not migration accelerators — the trap is picking 'more queues' or 'disable distribution keys' as shortcuts, when the real levers are COPY from S3, columnar formats, and post-load VACUUM/ANALYZE.
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 Amazon S3.
Option B is correct because the COPY command is the most efficient, parallelized way to bulk-load large datasets from Amazon S3 into Redshift, and it supports loading from multiple files in parallel to speed up the full load and minimize cutover downtime. Option C is correct because storing the source files in a columnar format such as Parquet lets COPY read only needed columns, compresses data heavily, and reduces I/O and load time compared with row-based CSV or JSON. Option D is correct because after a large load, running VACUUM re-sorts rows and reclaims space (restoring sort-key performance) and ANALYZE refreshes table statistics so the query planner produces efficient plans for the subsequent incremental workload. Option A is not appropriate because adding WLM queues does not by itself accelerate a single large load and can even reduce per-query resources; queue design is about workload isolation, not migration throughput. Option E is not appropriate because disabling distribution keys removes the ability to co-locate joins and causes data redistribution at query time, hurting performance; distribution keys should be chosen deliberately, not disabled.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of WLM queues to allow more concurrent loads.
Why it's wrong here
WLM queues govern query concurrency at runtime, not bulk load throughput or cutover duration. Adding queues cannot shorten the full load or incremental sync window. It is tempting because WLM tuning genuinely helps mixed analytical workloads, but the scenario demands migration acceleration mechanisms such as parallel COPY operations and change data capture.
- ✓
Use the COPY command to load data from Amazon S3.
Why this is correct
The COPY command loads data in parallel from Amazon S3 into Redshift, directly satisfying the requirement to minimise cutover downtime during the full load. Its massively parallel architecture ingests the 500-million-row tables far faster than row-by-row INSERT statements, compressing the migration window before incremental updates begin.
- ✓
Use columnar format (e.g., Parquet) for the data files in S3.
Why this is correct
Parquet’s columnar layout lets Redshift COPY read only referenced columns, cutting I/O and load time for wide tables (100 columns) across 500 million rows. This directly shortens the full-load phase, satisfying the requirement to minimise cutover downtime before incremental updates begin.
- ✓
Run VACUUM and ANALYZE commands after loading the data.
Why this is correct
VACUUM reclaims space from deleted rows and re-sorts unsorted data, while ANALYZE refreshes table statistics for the query planner. After a 500-million-row full load, this restores optimal distribution and sort order, keeping query performance predictable during the incremental-update phase without extending cutover downtime.
- ✗
Disable distribution keys on the target tables to simplify loading.
Why it's wrong here
Removing distribution keys forces every join and aggregation into broadcast or redistribution across all nodes, inflating load and query time on 500-million-row tables. Distribution keys exist to co-locate matching rows; they would be chosen deliberately for this schema. The option confuses simplifying ingestion with optimising a large-scale Redshift migration.
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
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.