- A
Create a BRIN index on signup_date.
BRIN indexes are lightweight and efficient for large tables with natural ordering, improving analytical queries with low write overhead.
- B
Change the index on email from B-tree to hash.
Hash indexes are efficient for equality queries and have lower maintenance overhead than B-tree.
- C
Partition the customers table by signup_date using range partitioning.
Partitioning can improve write performance by dividing the table into smaller pieces and helps analytical queries on date ranges.
- D
Remove the index on email to reduce write latency.
Why wrong: Removing the index would degrade read performance for email queries.
- E
Create a GIN index on email.
Why wrong: GIN indexes are for composite values like arrays, not for simple equality on email.
Quick Answer
The answer is to partition the customers table by signup_date using range partitioning, create a BRIN index on signup_date, and consider dropping or making the email index non-unique if possible. This combination improves write performance without sacrificing read performance because BRIN indexes are incredibly lightweight compared to B-tree indexes—they store summary metadata for blocks of data rather than every row, drastically reducing write overhead while still efficiently supporting analytical queries on ordered columns like signup_date. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of PostgreSQL indexing trade-offs and partitioning strategies for mixed OLTP and analytical workloads; a common trap is reflexively adding more B-tree indexes, which would worsen write latency. Remember the mnemonic “BRIN for Big, Ordered Data” to recall that BRIN indexes excel on large, naturally sorted tables where write speed matters, and pair them with range partitioning to isolate read-heavy email queries on smaller, more manageable partitions.
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.
A company runs a customer relationship management (CRM) application on Amazon RDS for PostgreSQL. The application has a table 'customers' with columns: id (primary key), name, email, signup_date, and status. The table has 10 million rows. The application frequently queries by email address. The company also needs to run analytical queries that aggregate customers by signup_date and status. The DBA notices that the index on email is heavily used and causes high write latency. The company wants to improve write performance without sacrificing read performance for email queries. Which THREE actions should the database specialist take? (Choose THREE.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"primary"Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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 BRIN index on signup_date.
Option A is correct because a BRIN (Block Range INdex) index on signup_date is ideal for large tables where data is naturally ordered by insertion time. BRIN indexes are much smaller and have lower write overhead than B-tree indexes, making them suitable for analytical queries on signup_date without significantly impacting write performance.
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.
- ✓
Create a BRIN index on signup_date.
Why this is correct
BRIN indexes are lightweight and efficient for large tables with natural ordering, improving analytical queries with low write overhead.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Change the index on email from B-tree to hash.
Why this is correct
Hash indexes are efficient for equality queries and have lower maintenance overhead than B-tree.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Partition the customers table by signup_date using range partitioning.
Why this is correct
Partitioning can improve write performance by dividing the table into smaller pieces and helps analytical queries on date ranges.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove the index on email to reduce write latency.
Why it's wrong here
Removing the index would degrade read performance for email queries.
- ✗
Create a GIN index on email.
Why it's wrong here
GIN indexes are for composite values like arrays, not for simple equality on email.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think removing the email index (Option D) is acceptable to reduce write latency, but the question explicitly states read performance for email queries must not be sacrificed, so the index must be retained and optimized instead.
Detailed technical explanation
How to think about this question
BRIN indexes work by summarizing the minimum and maximum values in each block range (default 128 pages), allowing the database to skip irrelevant blocks during scans. For time-series or append-only data like signup_date, this provides excellent compression and low maintenance cost. In contrast, B-tree indexes maintain a balanced tree structure that requires updating multiple pages on each INSERT, which is the primary cause of high write latency in this scenario.
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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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 BRIN index on signup_date. — Option A is correct because a BRIN (Block Range INdex) index on signup_date is ideal for large tables where data is naturally ordered by insertion time. BRIN indexes are much smaller and have lower write overhead than B-tree indexes, making them suitable for analytical queries on signup_date without significantly impacting write performance.
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.
Are there clue words in this question I should notice?
Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This DBS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DBS-C01 exam.
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