DEA-C01 Data Store Management Practice Question
A company uses Amazon Redshift for a data warehouse. They notice that queries are slow due to heavy data skew. Which optimization technique should be applied first?
⚠ Common exam trap
A common mix-up: candidates confuse distribution skew with sort key optimization or compression, mistakenly believing that improving data organization on disk (sort keys) or reducing I/O (compression) will fix uneven data distribution across nodes.
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
✓
Set an appropriate distribution style
Data skew occurs when rows are distributed unevenly across Redshift slices, causing some nodes to process far more data than others. Setting an appropriate distribution style (e.g., KEY, EVEN, or ALL) redistributes the data to balance the workload, directly addressing the root cause of the slowness. This is the first optimization to apply because skew is a fundamental distribution issue that other tuning steps cannot fix.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure workload management (WLM) queues
Why it's wrong here
WLM queues govern concurrency and memory allocation between query classes; they do not redistribute rows across slices, so skewed joins and aggregations remain bottlenecked on hot slices. WLM is the right first step when many concurrent queries contend for cluster resources, not when a single query's data distribution is uneven.
- ✗
Define sort keys on frequently filtered columns
Why it's wrong here
Sort keys reduce blocks scanned for range-filtered predicates and enable zone-map pruning, but they leave the skewed distribution of join or GROUP BY keys untouched, so one slice still processes most rows. Sort keys are correct when filtering on a column benefits from block skipping, not when redistribution is needed.
- ✓
Set an appropriate distribution style
Why this is correct
Data skew concentrates rows on some slices, so those slices do redundant work during joins and aggregations. Choosing a distribution style that spreads rows evenly, such as KEY on a high-cardinality column or ALL for small tables, addresses the root cause first.
- ✗
Apply compression encodings to columns
Why it's wrong here
Compression encodings shrink storage footprint and I/O volume per column, yet they do not change how rows are assigned to slices, so a skewed join key still overloads one slice. Compression is the right choice when reducing scanned bytes for wide, repetitive columns, not for balancing workload across slices.
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Same concept, more angles
1 more way this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Check the STL_LOAD_ERRORS table for load failures
- B.Query the SVV_TABLE_INFO table to see table size
- ✓ C.Query the SVV_DISKUSAGE table to examine data distribution across slices
- D.Review the WLM configuration in the parameter group
Why C: 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.
JA
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.