DEA-C01 Data Store Management Practice Question
A data engineer is configuring an Amazon Redshift cluster and needs to optimize query performance for complex analytical queries that involve large joins. The engineer wants to reduce the amount of data movement during query execution. Which two actions should the engineer take? (Choose two.)
⚠ Common exam trap
The trap here is assuming that adding more nodes or enabling concurrency scaling will reduce data movement during joins, but those actions address capacity and concurrency, not join efficiency.
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 frequently used in join and filter conditions.
To reduce data movement during joins in Amazon Redshift, the engineer should choose a distribution style that colocates joined tables on the same node slices and define sort keys on columns used in joins and filters. These actions minimize network traffic and enable efficient merge joins. Increasing nodes, enabling concurrency scaling, or using columnar storage do not directly address data movement within a query.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of nodes in the cluster to add more compute resources.
Why it's wrong here
Adding nodes increases compute capacity and storage but does not inherently reduce data movement during joins. In fact, more nodes can increase network traffic if data is not distributed optimally. While scaling can improve performance, it is not a targeted solution for reducing data movement in joins. The focus should be on distribution and sort keys.
- ✓
Define sort keys on columns frequently used in join and filter conditions.
Why this is correct
Sort keys determine the order in which data is stored on disk. When sort keys are defined on columns used in joins and filters, Redshift can use zone maps to skip irrelevant blocks and perform efficient merge joins. This reduces I/O and data movement, enhancing query performance for complex analytical workloads.
- ✗
Enable concurrency scaling to handle concurrent queries.
Why it's wrong here
Concurrency scaling adds transient clusters to handle bursts of concurrent queries, improving throughput. However, it does not address data movement within a single complex query. The goal is to optimize join performance by reducing data movement, which is achieved through distribution and sort keys, not concurrency scaling.
- ✓
Choose a distribution style that colocates joined tables on the same node slices.
Why this is correct
Choosing an appropriate distribution style, such as distributing on the join key, ensures that matching rows from joined tables are located on the same node slices. This minimizes data movement across the network during joins, significantly improving query performance. It is a best practice for large join operations in Redshift.
- ✗
Use columnar storage for all tables.
Why it's wrong here
Amazon Redshift already stores data in a columnar format by default. This is not a configuration action the engineer can take to further reduce data movement. While columnar storage benefits analytical queries, it does not specifically minimize join data movement. The key actions are distribution and sort keys.
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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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