This DEA-C01 practice question tests your understanding of data store management. 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.
Exhibit
CREATE TABLE sales (
id INT NOT NULL,
product_id INT NOT NULL,
sale_date DATE NOT NULL,
amount DECIMAL(10,2),
region VARCHAR(20)
) DISTKEY(product_id) SORTKEY(sale_date);
-- Query:
SELECT region, SUM(amount)
FROM sales
WHERE sale_date BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY region;
Refer to the exhibit. A data engineer is analyzing a query performance issue on an Amazon Redshift table. The table 'sales' has 100 million rows. The query is performing a full table scan. Which optimization should the engineer apply to improve query performance?
Exhibit
CREATE TABLE sales (
id INT NOT NULL,
product_id INT NOT NULL,
sale_date DATE NOT NULL,
amount DECIMAL(10,2),
region VARCHAR(20)
) DISTKEY(product_id) SORTKEY(sale_date);
-- Query:
SELECT region, SUM(amount)
FROM sales
WHERE sale_date BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY region;
A
Change DISTKEY to region.
Why wrong: Distribution key affects data distribution across nodes, not scan efficiency for this query.
B
Use an interleaved sort key on (sale_date, region).
Why wrong: Interleaved sort keys are for multi-dimensional queries; range queries may not benefit as much.
C
Use a compound sort key on (sale_date, region).
Compound sort key on sale_date first enables efficient range restriction, then region for aggregation.
D
Change DISTSTYLE to ALL.
Why wrong: ALL distribution replicates data to all nodes, increasing storage and not improving scan.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a compound sort key on (sale_date, region).
The query is performing a full table scan, which indicates that Redshift cannot efficiently prune blocks. A compound sort key on (sale_date, region) orders rows by sale_date first, then by region, allowing Redshift to skip large portions of data when filtering on the leading column (sale_date). This minimizes the number of blocks scanned and dramatically improves query performance for range-based or equality filters on the leading column.
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 DISTKEY to region.
Why it's wrong here
Distribution key affects data distribution across nodes, not scan efficiency for this query.
✗
Use an interleaved sort key on (sale_date, region).
Why it's wrong here
Interleaved sort keys are for multi-dimensional queries; range queries may not benefit as much.
✓
Use a compound sort key on (sale_date, region).
Why this is correct
Compound sort key on sale_date first enables efficient range restriction, then region for aggregation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change DISTSTYLE to ALL.
Why it's wrong here
ALL distribution replicates data to all nodes, increasing storage and not improving scan.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse distribution keys (which control data placement across nodes) with sort keys (which control row order within a node), leading them to choose DISTKEY or DISTSTYLE changes when the real issue is block pruning from a full table scan.
Detailed technical explanation
How to think about this question
In Amazon Redshift, zone maps track the minimum and maximum values of sort key columns for each 1 MB block. A compound sort key on (sale_date, region) enables Redshift to use these zone maps to skip blocks that do not fall within the query's filter range, reducing I/O. For example, if a query filters on sale_date between '2023-01-01' and '2023-01-31', Redshift can skip all blocks whose max sale_date is before '2023-01-01' or min sale_date is after '2023-01-31', even if the table has 100 million rows.
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.
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: Use a compound sort key on (sale_date, region). — The query is performing a full table scan, which indicates that Redshift cannot efficiently prune blocks. A compound sort key on (sale_date, region) orders rows by sale_date first, then by region, allowing Redshift to skip large portions of data when filtering on the leading column (sale_date). This minimizes the number of blocks scanned and dramatically improves query performance for range-based or equality filters on the leading column.
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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Question Discussion
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