- A
Change the distribution style of both tables to ALL
Why wrong: ALL replicates the entire table to every node, which is inefficient for large tables.
- B
Change the distribution style of both tables to KEY on the join column
KEY distribution on the join column ensures matching rows are on the same node, reducing redistribution.
- C
Change the distribution style of both tables to EVEN
Why wrong: EVEN distribution spreads data round-robin, which does not collocate join keys.
- D
Add a sort key on the join column
Why wrong: Sort keys improve data pruning but do not reduce redistribution during joins.
DEA-C01 Data Store Management Practice Question
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.
A data engineer is troubleshooting a slow-running query on an Amazon Redshift cluster. The query involves joining two large tables. The engineer notices that the query plan shows a large number of distribution and broadcast operations. Which design change would most likely improve query performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 distribution style of both tables to KEY on the join column
Option B is correct because changing the distribution style of both tables to KEY on the join column ensures that rows with the same join key value are co-located on the same node. This eliminates the need for expensive broadcast or redistribution operations during the join, as Redshift can perform the join locally on each slice without moving data across the network.
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 the distribution style of both tables to ALL
Why it's wrong here
ALL replicates the entire table to every node, which is inefficient for large tables.
- ✓
Change the distribution style of both tables to KEY on the join column
Why this is correct
KEY distribution on the join column ensures matching rows are on the same node, reducing redistribution.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the distribution style of both tables to EVEN
Why it's wrong here
EVEN distribution spreads data round-robin, which does not collocate join keys.
- ✗
Add a sort key on the join column
Why it's wrong here
Sort keys improve data pruning but do not reduce redistribution during joins.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse distribution and sort keys, thinking a sort key on the join column will reduce data movement, when in fact only distribution key alignment eliminates broadcast/redistribution operations in the query plan.
Detailed technical explanation
How to think about this question
Under the hood, Redshift's MPP architecture distributes table slices across nodes based on the distribution style. When joining two tables on a KEY distribution column, Redshift can perform a collocated join where each slice processes only the rows it owns, avoiding network data movement. A real-world scenario is joining a large fact table with a dimension table on a customer_id; if both are distributed by customer_id, the join is local, whereas EVEN or mismatched KEY styles trigger redistribution, which can be a major bottleneck in high-concurrency workloads.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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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Data Store Management — study guide chapter
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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 distribution style of both tables to KEY on the join column — Option B is correct because changing the distribution style of both tables to KEY on the join column ensures that rows with the same join key value are co-located on the same node. This eliminates the need for expensive broadcast or redistribution operations during the join, as Redshift can perform the join locally on each slice without moving data across the network.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
This DEA-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 DEA-C01 exam.
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