Quick Answer
The correct answer is to partition the table by month using PostgreSQL declarative partitioning. This design change directly improves time-series query performance because partition pruning allows the query planner to scan only the partition containing the last hour’s data, rather than the entire 500-million-row table. Even with well-tuned indexes on event_time and event_type, a monolithic table forces the database to navigate a massive index structure, whereas partitioning physically separates data by time range, dramatically reducing I/O. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of how to optimize large time-series workloads on Amazon RDS for PostgreSQL, often as a trap where candidates over-index instead of partitioning. A common memory tip: “Partition by time to prune the grind”—if your query filters on a time range, partition on that same column to let PostgreSQL skip irrelevant data automatically.
This DBS-C01 practice question tests your understanding of workload-specific database design. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Add a composite index on (event_type, event_time)
Why wrong: Index may not help if old data dominates.
Partition the table by month using PostgreSQL declarative partitioning
Partition pruning limits scans to relevant partitions.
Migrate to Amazon DynamoDB with TTL
Why wrong: NoSQL may not support required queries.
Upgrade to a larger RDS instance
Why wrong: Vertical scaling is not a design improvement.
Answer choices
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
PostgreSQL declarative partitioning uses partition pruning at query planning time, where the planner examines WHERE clauses on the partition key (e.g., event_time) to eliminate irrelevant partitions. For a monthly partition scheme, a query for the last hour will access only the current month's partition, which might contain, for example, 10 million rows instead of 500 million, reducing I/O by orders of magnitude. Additionally, each partition can have its own indexes, and maintenance operations like VACUUM and ANALYZE can run on individual partitions, reducing bloat and improving performance over time.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
The correct answer is: Partition the table by month using PostgreSQL declarative partitioning — Option B is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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