The correct answer is to set an appropriate distribution style, as this directly addresses the root cause of data skew in Amazon Redshift. Data skew occurs when rows are distributed unevenly across slices, overloading certain nodes while others remain idle, which degrades query performance. By choosing a distribution style—KEY for joining large tables on a common column, EVEN for uniform distribution when no clear join key exists, or ALL for small dimension tables—you rebalance the workload across the cluster. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding that distribution optimization is the foundational step before tuning sort keys or compression, because skew is a structural issue that no other optimization can fix. A common trap is jumping to vacuum or sort key changes, but those only help after data is evenly distributed. Memory tip: think “Distribute first, sort second”—balance the load before you organize the rows.
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 company uses Amazon Redshift for a data warehouse. They notice that queries are slow due to heavy data skew. Which optimization technique should be applied first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set an appropriate distribution style
Data skew occurs when rows are distributed unevenly across Redshift slices, causing some nodes to process far more data than others. Setting an appropriate distribution style (e.g., KEY, EVEN, or ALL) redistributes the data to balance the workload, directly addressing the root cause of the slowness. This is the first optimization to apply because skew is a fundamental distribution issue that other tuning steps cannot fix.
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.
✗
Configure workload management (WLM) queues
Why it's wrong here
WLM manages concurrency, not data distribution.
✗
Define sort keys on frequently filtered columns
Why it's wrong here
Sort keys improve performance on sorted columns but don't directly address data skew.
✓
Set an appropriate distribution style
Why this is correct
Correct distribution style reduces data skew and improves query performance.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply compression encodings to columns
Why it's wrong here
Compression reduces storage and I/O, but not skew.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse distribution skew with sort key optimization or compression, mistakenly believing that improving data organization on disk (sort keys) or reducing I/O (compression) will fix uneven data distribution across nodes.
Detailed technical explanation
How to think about this question
Under the hood, Redshift distributes table rows to compute nodes using the distribution key's hash value modulo the number of slices. If a distribution key has low cardinality or is heavily skewed (e.g., a status column with 99% 'active'), the hash distribution concentrates most rows on a few slices, causing severe performance bottlenecks. Choosing a high-cardinality distribution key or using EVEN distribution (round-robin) can eliminate skew, while ALL distribution replicates small tables to all nodes to avoid broadcast joins.
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: Set an appropriate distribution style — Data skew occurs when rows are distributed unevenly across Redshift slices, causing some nodes to process far more data than others. Setting an appropriate distribution style (e.g., KEY, EVEN, or ALL) redistributes the data to balance the workload, directly addressing the root cause of the slowness. This is the first optimization to apply because skew is a fundamental distribution issue that other tuning steps cannot fix.
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: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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