DEA-C01 Data Store Management Practice Question
A data engineer is troubleshooting an Amazon Redshift cluster that is running out of disk space. The engineer runs STV_PARTITIONS and notices that some slices have significantly more data than others. What is the most likely cause and solution?
⚠ Common exam trap
Many candidates confuse sort keys (which improve query performance via zone maps) with distribution keys (which control data placement across slices), leading them to incorrectly select sort key redefinition as the fix for disk space skew.
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
✓
Data distribution skew due to uneven distribution style; change distribution style to EVEN or correct KEY
B is correct because STV_PARTITIONS shows per-slice disk usage, and significant variation indicates data distribution skew. Uneven distribution causes some slices to fill faster, leading to premature disk-full errors. Changing the distribution style to EVEN (for tables without join keys) or correcting the KEY distribution style (using a high-cardinality, evenly distributed column) rebalances data across slices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Poorly chosen sort keys; redefine sort keys
Why it's wrong here
Sort keys order rows within a block for zone-map pruning; they do not determine which slice stores a row, so skewed slice sizes persist. Redefining them suits range-filter query performance. Slice skew is resolved by choosing a distribution key with high cardinality and even spread.
- ✓
Data distribution skew due to uneven distribution style; change distribution style to EVEN or correct KEY
Why this is correct
Uneven slice storage indicates distribution skew: rows cluster on certain slices under a poor KEY distribution style. Redistributing with EVEN, or choosing a higher-cardinality KEY column, spreads data evenly across slices and resolves the disk-space imbalance.
- ✗
Some nodes are underutilized; add more nodes
Why it's wrong here
Uneven slice distribution stems from a poor distribution key or sort key skew, not node count; adding nodes redistributes slices but leaves the skew pattern, so the hot slices remain oversized. Adding nodes suits genuine capacity or compute shortfalls where data is already evenly spread across existing slices.
- ✗
Concurrency scaling is disabled; enable concurrency scaling
Why it's wrong here
Concurrency scaling adds transient compute for queued read queries; it does not redistribute stored data across slices, so skew and disk pressure persist. Enabling it suits bursty concurrent read workloads needing extra query throughput, not storage imbalance caused by a skewed distribution key.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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