DEA-C01 Data Operations and Support Practice Question
A company runs a Redshift cluster for analytics. The data engineering team notices that COPY commands from S3 are failing for large files (>1 GB) with the error 'S3ServiceException: SlowDown'. What is the most effective solution?
⚠ Common exam trap
DEA-C01 often tests the misconception that increasing cluster size or using Spectrum solves COPY performance issues, when the root cause is often S3 request throttling due to large files or hot prefixes.
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
✓
Split the large files into smaller parts (e.g., 100 MB each) and use parallel COPY.
The 'SlowDown' error from S3 occurs when too many requests are made to the same S3 prefix, causing throttling. Splitting large files into smaller parts (e.g., 100 MB) and using parallel COPY distributes the load across multiple slices in Redshift, reducing the request rate per prefix and avoiding throttling.
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 Redshift Spectrum to query the data directly in S3.
Why it's wrong here
Redshift Spectrum queries external S3 data without loading it, so it cannot resolve throttling on COPY ingestion into cluster tables. It suits ad-hoc analysis of data left in S3, avoiding load time. The SlowDown error stems from excessive concurrent S3 requests, which Spectrum bypasses rather than fixes.
- ✗
Enable automatic compression on the target tables.
Why it's wrong here
Compression reduces storage and I/O volume but does not change how many concurrent requests Redshift issues to S3, so throttling persists. Compression is correct when reducing scan bytes or storage cost, not for resolving S3 request-rate limits.
- ✗
Increase the number of Redshift nodes to distribute the load.
Why it's wrong here
Adding compute nodes does not address S3's per-prefix request-rate throttling, which triggers SlowDown regardless of cluster size. It is tempting because scaling Redshift relieves query and slice contention, and would be right for CPU- or memory-bound workloads. Here the fix is spreading COPY input across multiple S3 prefixes or using a manifest.
- ✓
Split the large files into smaller parts (e.g., 100 MB each) and use parallel COPY.
Why this is correct
S3 SlowDown signals request-rate throttling when Redshift reads very large objects. Splitting files into roughly 100 MB parts and issuing parallel COPY lets Redshift distribute the load across slices, avoiding the throttling that stalls single large-file reads.
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
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