- A
Partition the table by month and query only the relevant partitions.
Why wrong: Partition pruning helps but still requires scanning 30 days of data (one partition). Aggregation over that partition can be slow.
- B
Create a materialized view that stores daily sales aggregates and refresh it nightly.
The report reads pre-computed aggregates, reducing query time drastically.
- C
Add a composite index on the date column and the sales amount column.
Why wrong: An index could help range scans but the query still needs to aggregate many rows, possibly still slow.
- D
Upgrade the RDS instance to a larger size with more vCPUs and memory.
Why wrong: Scaling up is expensive and may not reduce query time to under 30 seconds due to full table scan.
Question 785 of 1,730
DBS-C01 Workload-Specific Database Design Practice Question
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.
An e-commerce platform uses Amazon RDS for PostgreSQL to store order data. The database has a table "orders" with 500 million rows. The application runs a report query that aggregates daily sales for the last 30 days. The query currently scans the entire table and takes 15 minutes to complete. The team needs to reduce the query time to under 30 seconds. Which solution is MOST cost-effective?
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
✓
Create a materialized view that stores daily sales aggregates and refresh it nightly.
Option B is correct because a materialized view precomputes and stores the daily sales aggregates, allowing the application to query the precomputed result set directly instead of scanning 500 million rows. Refreshing the materialized view nightly (e.g., using pg_cron or a scheduled lambda) ensures the data is fresh enough for the report while keeping query time under 30 seconds. This approach avoids the cost of larger instances or complex partitioning and is the most cost-effective solution for a read-heavy, periodic aggregation workload.
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 month and query only the relevant partitions.
Why it's wrong here
Partition pruning helps but still requires scanning 30 days of data (one partition). Aggregation over that partition can be slow.
- ✓
Create a materialized view that stores daily sales aggregates and refresh it nightly.
Why this is correct
The report reads pre-computed aggregates, reducing query time drastically.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add a composite index on the date column and the sales amount column.
Why it's wrong here
An index could help range scans but the query still needs to aggregate many rows, possibly still slow.
- ✗
Upgrade the RDS instance to a larger size with more vCPUs and memory.
Why it's wrong here
Scaling up is expensive and may not reduce query time to under 30 seconds due to full table scan.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose partitioning (Option A) thinking it will reduce scan time, but they overlook that partitioning does not precompute aggregates and still requires scanning multiple partitions, whereas a materialized view directly addresses the aggregation bottleneck at a lower cost.
Detailed technical explanation
How to think about this question
Materialized views in PostgreSQL store the result set physically, and refreshing them can be done concurrently (REFRESH MATERIALIZED VIEW CONCURRENTLY) to avoid locking reads, though this requires a unique index. For a 500-million-row table, the nightly refresh might take several minutes, but that is acceptable as it runs during off-peak hours. The materialized view can be indexed further (e.g., on the date column) to speed up the refresh query itself, and the application queries only the precomputed 30 rows, achieving sub-second response times.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
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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: Create a materialized view that stores daily sales aggregates and refresh it nightly. — Option B is correct because a materialized view precomputes and stores the daily sales aggregates, allowing the application to query the precomputed result set directly instead of scanning 500 million rows. Refreshing the materialized view nightly (e.g., using pg_cron or a scheduled lambda) ensures the data is fresh enough for the report while keeping query time under 30 seconds. This approach avoids the cost of larger instances or complex partitioning and is the most cost-effective solution for a read-heavy, periodic aggregation workload.
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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