DBS-C01 Workload-Specific Database Design Practice Question
A company runs a time-series application on Amazon RDS for PostgreSQL. The table 'events' has 500 million rows and is queried by event_time and event_type. Queries for the last hour are slow despite indexing. Which design change would most improve query performance?
⚠ Common exam trap
It's easy for candidates to assume adding a composite index is sufficient for all query patterns, but for time-series data with a large table and narrow time-range queries, partition pruning provides a far more significant reduction in scanned data than any index can achieve.
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
✓
Partition the table by month using PostgreSQL declarative partitioning
Partitioning the 'events' table by month using PostgreSQL declarative partitioning allows the query planner to prune partitions that do not contain data for the last hour. This dramatically reduces the number of rows scanned, even with a large table of 500 million rows, and directly addresses the slow query performance for time-range queries. Indexing alone cannot overcome the overhead of scanning a massive monolithic table for a narrow time window.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a composite index on (event_type, event_time)
Why it's wrong here
Index may not help if old data dominates.
- ✓
Partition the table by month using PostgreSQL declarative partitioning
Why this is correct
Partition pruning limits scans to relevant partitions.
- ✗
Migrate to Amazon DynamoDB with TTL
Why it's wrong here
NoSQL may not support required queries.
- ✗
Upgrade to a larger RDS instance
Why it's wrong here
Vertical scaling is not a design improvement.
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