- A
Partition the table by time using PostgreSQL table partitioning
Partitioning by time allows partition pruning, significantly improving query performance on timestamp filters.
- B
Migrate to Amazon Aurora PostgreSQL and enable parallel query
Why wrong: Parallel query can speed up some queries but partitioning is a more fundamental optimization for time-series data.
- C
Add more indexes on the timestamp column
Why wrong: Additional indexes on the same column are redundant and may degrade write performance.
- D
Create a read replica and direct queries to the replica
Why wrong: Read replicas distribute read load but do not improve performance of individual queries against large tables.
Question 555 of 1,730
Improving PostgreSQL Time Series Query Performance with Table Partitioning
This DBS-C01 practice question tests your understanding of workload-specific database design. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 uses Amazon RDS for PostgreSQL to store sensor data. Each sensor sends a row every second. The table has grown to 500 GB and queries filtering on a timestamp column are slow even with an index. The team wants to improve query performance while keeping the data online. Which approach should they take?
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 time using PostgreSQL table partitioning
PostgreSQL table partitioning by time (e.g., by day or month) allows the query planner to prune partitions that do not match the timestamp filter, drastically reducing the amount of data scanned. This is the most effective approach for time-series data because it maintains online access and improves query performance without requiring a migration or additional replicas.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the table by time using PostgreSQL table partitioning
Why this is correct
Partitioning by time allows partition pruning, significantly improving query performance on timestamp filters.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Migrate to Amazon Aurora PostgreSQL and enable parallel query
Why it's wrong here
Parallel query can speed up some queries but partitioning is a more fundamental optimization for time-series data.
- ✗
Add more indexes on the timestamp column
Why it's wrong here
Additional indexes on the same column are redundant and may degrade write performance.
- ✗
Create a read replica and direct queries to the replica
Why it's wrong here
Read replicas distribute read load but do not improve performance of individual queries against large tables.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume adding indexes or using read replicas will solve performance issues for large time-series tables, but they fail to recognize that partitioning directly reduces the data scanned per query, which is the root cause of slow filtering on timestamp columns.
Detailed technical explanation
How to think about this question
PostgreSQL declarative partitioning (introduced in PostgreSQL 10) uses constraint exclusion to eliminate partitions at planning time. For time-series data, range partitioning on the timestamp column allows the planner to skip entire partitions that fall outside the query's WHERE clause, reducing sequential scan overhead. In a real-world scenario with 500 GB of sensor data, daily partitions would each contain roughly 1.4 GB (assuming 1 row/second per sensor), making queries that filter on the last hour scan only a single partition instead of the entire table.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Partition the table by time using PostgreSQL table partitioning — PostgreSQL table partitioning by time (e.g., by day or month) allows the query planner to prune partitions that do not match the timestamp filter, drastically reducing the amount of data scanned. This is the most effective approach for time-series data because it maintains online access and improves query performance without requiring a migration or additional replicas.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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