- A
Change distribution style to KEY on customer_id.
Why wrong: Distribution key helps with joins and data distribution, not filtering within a table.
- B
Change distribution style to EVEN.
Why wrong: EVEN distribution spreads data evenly but doesn't improve filter performance.
- C
Change the sort key to include customer_id as a compound sort key.
A compound sort key with customer_id first will optimize queries filtering by customer_id.
- D
Change the sort key to an interleaved sort key on date and customer_id.
Why wrong: Interleaved sort key can help but is more complex and may not be necessary if customer_id is the primary filter.
Question 1,131 of 1,786
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 Redshift for its data warehouse. The data engineer notices that the most frequently accessed table is sorted by date, but queries often filter by customer_id. The table has 500 million rows and uses AUTO distribution style. What change would MOST improve query performance?
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
✓
Change the sort key to include customer_id as a compound sort key.
Since queries frequently filter by customer_id but the table is sorted only by date, Redshift must scan all rows that match the date range and then filter by customer_id. By adding customer_id as a compound sort key (date, customer_id), Redshift can use zone maps to skip blocks that don't contain the requested customer_id within the date range, dramatically reducing the number of rows scanned and improving query 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.
- ✗
Change distribution style to KEY on customer_id.
Why it's wrong here
Distribution key helps with joins and data distribution, not filtering within a table.
- ✗
Change distribution style to EVEN.
Why it's wrong here
EVEN distribution spreads data evenly but doesn't improve filter performance.
- ✓
Change the sort key to include customer_id as a compound sort key.
Why this is correct
A compound sort key with customer_id first will optimize queries filtering by customer_id.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the sort key to an interleaved sort key on date and customer_id.
Why it's wrong here
Interleaved sort key can help but is more complex and may not be necessary if customer_id is the primary filter.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume distribution style (KEY or EVEN) is the primary lever for query performance on filtered columns, when in fact sort keys—especially compound sort keys—are far more impactful for reducing scanned data in range-filtered queries.
Detailed technical explanation
How to think about this question
In Amazon Redshift, a compound sort key uses the first column for the primary sort order, and zone maps store the minimum and maximum values of each column per 1 MB block. When a query filters on the leading sort key (date) and the second sort key (customer_id), Redshift can prune blocks that don't contain the customer_id within the date range, reducing I/O. For a table with 500 million rows, this block-level elimination can reduce scanned data by orders of magnitude compared to scanning all rows that match the date range.
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the sort key to include customer_id as a compound sort key. — Since queries frequently filter by customer_id but the table is sorted only by date, Redshift must scan all rows that match the date range and then filter by customer_id. By adding customer_id as a compound sort key (date, customer_id), Redshift can use zone maps to skip blocks that don't contain the requested customer_id within the date range, dramatically reducing the number of rows scanned and improving query performance.
What should I do if I get this DEA-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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