Redshift Table Design — Sort Keys and Distribution
A company is using Amazon Redshift for its data warehouse. The data engineering team needs to improve query performance for a large fact table that is frequently joined with multiple dimension tables. Which THREE strategies should be considered?
Quick Answer
The answer is to define sort keys on columns used in WHERE clauses, apply columnar compression, and choose an appropriate distribution style. Sort keys are correct because they enable Redshift’s zone maps to skip entire disk blocks that don’t match filter conditions, drastically reducing I/O for large fact tables. Columnar compression further shrinks storage and speeds scans by encoding similar data values efficiently, while a well-chosen distribution style—like KEY on a join column—minimizes data shuffling during joins with dimension tables. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of how physical table design directly impacts Redshift’s massively parallel processing architecture; a common trap is to focus only on indexing or caching, which Redshift does not use. Remember the mnemonic “Sort, Squeeze, Spread”—sort keys for pruning, compression for squeezing data, and distribution for spreading work across nodes.
⚠ Common exam trap
Many exam-takers assume DISTSTYLE EVEN is always the best choice for performance, but for frequently joined fact tables, a distribution key aligned with the join columns is critical to avoid network-heavy data shuffling.
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
✓
Define sort keys on columns used in WHERE clauses.
Option A is correct because defining sort keys on columns frequently used in WHERE clauses allows Redshift to use zone maps and block-level metadata to skip irrelevant blocks, dramatically reducing I/O for filtered queries on the large fact table. Option D is correct because choosing a distribution key based on the fact table's join columns co-locates matching rows on the same node slice, enabling collocated joins that avoid costly data redistribution (broadcast or shuffle) across the cluster. Option E is correct because columnar compression reduces the amount of data read from disk and improves I/O efficiency, which is especially beneficial for large fact tables scanned by analytical queries. Option B is not ideal here because DISTSTYLE EVEN spreads rows uniformly but does not co-locate join keys, so joins with dimension tables require network redistribution, hurting performance for frequent joins. Option C is not a targeted strategy for join performance; adding nodes increases compute and storage capacity but does not by itself address sort keys, distribution keys, or compression, and may not resolve join-related bottlenecks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define sort keys on columns used in WHERE clauses.
Why this is correct
Sort keys physically order rows on disk by the chosen columns, so range and equality predicates in WHERE clauses skip irrelevant blocks via zone maps. This reduces the rows scanned for the large fact table, directly improving the filtered queries feeding the joins.
- ✗
Use DISTSTYLE EVEN to distribute data evenly.
Why it's wrong here
DISTSTYLE EVEN spreads rows round-robin, so join rows land on different slices and require network redistribution; KEY distribution on the join column colocates them. It is tempting because EVEN does balance storage and suits tables never joined on a common column.
- ✗
Increase the number of nodes in the cluster.
Why it's wrong here
Adding nodes increases compute and storage capacity, but the stem targets join performance on a large fact table, which distribution and sort keys address directly. It is tempting because scaling out does raise throughput, and it would be correct for a cluster that is genuinely CPU- or memory-bound.
- ✓
Choose an appropriate distribution key based on join columns.
Why this is correct
Distribution keys control which slice stores each row; choosing a column frequently used in joins colocates matching rows on the same slice. This lets Redshift perform collocated joins without broadcasting or redistributing the large fact table across nodes, cutting network and query time.
- ✓
Apply columnar compression to reduce storage and I/O.
Why this is correct
Columnar compression stores each column's values contiguously and encodes them, so Redshift reads far fewer blocks when scanning the fact table's joined columns. This directly reduces I/O volume, the dominant cost in the large fact-table scans and joins described.
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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 uses Amazon Redshift for data warehousing. The data engineering team notices that queries are slow due to high disk I/O. The team wants to improve query performance without changing the cluster configuration. Which action should the team take?
medium- A.Increase the number of nodes in the cluster.
- ✓ B.Redesign tables with appropriate sort keys and distribution styles.
- C.Run the ANALYZE command to update table statistics.
- D.Run the VACUUM command to reclaim disk space.
Why B: Redesigning tables with appropriate sort keys and distribution styles directly addresses high disk I/O by minimizing data scanning and reducing data movement across nodes. Sort keys enable Redshift to skip irrelevant blocks via zone maps, while distribution styles (KEY, ALL, EVEN) optimize data locality for joins and aggregations, reducing I/O without changing cluster configuration.
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.