DEA-C01 Data Store Management Practice Question
A company runs a data warehouse on Amazon Redshift. Queries are slow, and the team suspects data distribution is skewed. Which approach would best help identify distribution skew?
⚠ Common exam trap
It's easy for candidates to confuse table-level metadata (SVV_TABLE_INFO) with slice-level distribution data (SVV_DISKUSAGE), assuming overall table size alone can reveal skew, when in fact only per-slice block counts expose uneven data distribution.
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
✓
Query the SVV_DISKUSAGE table to examine data distribution across slices
The SVV_DISKUSAGE table provides per-slice data distribution information, allowing you to identify skew by comparing the number of blocks allocated to each slice for a given table. In Amazon Redshift, data is distributed across slices based on the distribution key, and significant variation in block counts across slices indicates distribution skew, which can cause query performance degradation due to uneven workload distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Check the STL_LOAD_ERRORS table for load failures
Why it's wrong here
STL_LOAD_ERRORS records COPY failures and parse errors, not row distribution across slices, so it cannot reveal skew. It is tempting because load diagnostics are the first place to look when ingestion misbehaves, and it would be the right choice if the symptom were rejected rows or malformed input rather than slow queries.
- ✗
Query the SVV_TABLE_INFO table to see table size
Why it's wrong here
SVV_TABLE_INFO reports table size, encoding and skew rows, but its skew figure is table-level and does not reveal which slice or distribution key causes imbalance. Query SVV_TABLE_INFO for storage metrics; to diagnose distribution skew, examine slice-level row counts via STV_TBL_PERM or SVV_DISKUSAGE.
- ✓
Query the SVV_DISKUSAGE table to examine data distribution across slices
Why this is correct
SVV_DISKUSAGE reports per-slice row counts and disk usage, directly exposing uneven distribution across slices that causes skew. Unlike planner-oriented views such as SVV_TABLE_INFO, it shows physical storage per slice, satisfying the need to confirm skew empirically before choosing a distribution key.
- ✗
Review the WLM configuration in the parameter group
Why it's wrong here
WLM configuration governs query queues, concurrency and memory allocation, not how rows are hashed to slices, so it cannot expose skew. It is tempting because misconfigured queues do cause slow queries, and reviewing WLM would be correct if the problem were queue waits or memory spills rather than uneven data distribution.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.