DEA-C01 Data Operations and Support Practice Question
A company is running a Redshift cluster and wants to improve query performance for a frequently used dashboard. Which THREE approaches are recommended?
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
✓
Apply column compression encoding
The recommended approaches for improving Redshift query performance include applying column compression encoding (Option B) to reduce I/O, defining sort keys on columns used in WHERE clauses (Option C) to enable range-restricted scans, and choosing an appropriate distribution key for large tables (Option E) to minimize data movement between nodes. Enabling concurrency scaling (Option A) primarily helps with handling multiple concurrent queries but does not directly improve the performance of an individual query. Adding more nodes (Option D) can increase overall capacity but is often not the most cost-effective or immediate solution for performance issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable concurrency scaling
Why it's wrong here
Helps with concurrent queries, not single query speed.
- ✓
Apply column compression encoding
Why this is correct
Reduces I/O and storage.
- ✓
Define sort keys on columns used in WHERE clauses
Why this is correct
Enables min/max pruning.
- ✗
Add more nodes to the cluster
Why it's wrong here
Not a tuning recommendation; costly.
- ✓
Choose an appropriate distribution key for large tables
Why this is correct
Reduces data shuffling.
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