The correct approach is to query the SVV_DISKUSAGE table to examine data distribution across slices. This system table reveals how many 1 MB disk blocks each slice holds for a given table, and when you compare block counts across slices, significant variation directly indicates data skew. In Amazon Redshift, data is distributed according to the table’s distribution key, and uneven block allocation forces some slices to process far more data than others, degrading query performance. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your ability to diagnose performance issues using system tables rather than relying on STL or STV views; a common trap is confusing SVV_DISKUSAGE with STV_BLOCKLIST, but remember that SVV_DISKUSAGE aggregates per-slice block counts for quick skew analysis. Memory tip: “SVV” stands for “System Virtual View” — think “Skew Via Volume” to recall that this view shows volume (block count) per slice.
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 runs a data warehouse on Amazon Redshift. Queries are slow, and the team suspects data distribution is skewed. Which approach would best help identify distribution skew?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Query the SVV_DISKUSAGE table to examine data distribution across slices
Option C is correct because the SVV_DISKUSAGE table provides per-slice data distribution information, allowing you to identify skew by comparing the number of blocks allocated to each slice for a given table. In Amazon Redshift, data is distributed across slices based on the distribution key, and significant variation in block counts across slices indicates distribution skew, which can cause query performance degradation due to uneven workload distribution.
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.
✗
Check the STL_LOAD_ERRORS table for load failures
Why it's wrong here
Load errors do not indicate skew.
✗
Query the SVV_TABLE_INFO table to see table size
Why it's wrong here
SVV_TABLE_INFO shows table size, not distribution across slices.
✓
Query the SVV_DISKUSAGE table to examine data distribution across slices
Why this is correct
SVV_DISKUSAGE provides per-slice disk usage, helping identify skew.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Review the WLM configuration in the parameter group
Why it's wrong here
WLM manages query queues, not data distribution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse table-level metadata (SVV_TABLE_INFO) with slice-level distribution data (SVV_DISKUSAGE), assuming overall table size alone can reveal skew, when in fact only per-slice block counts expose uneven data distribution.
Trap categories for this question
Command / output trap
SVV_TABLE_INFO shows table size, not distribution across slices.
Detailed technical explanation
How to think about this question
Under the hood, Amazon Redshift distributes table rows across slices using distribution styles (KEY, ALL, EVEN). The SVV_DISKUSAGE table exposes the number of 1 MB blocks used per slice per table; a high variance in block counts (e.g., one slice having 10x more blocks than others) indicates severe skew. In real-world scenarios, choosing an inappropriate distribution key (e.g., a column with few distinct values) can lead to such skew, causing some slices to process far more data than others and bottlenecking query execution.
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: Query the SVV_DISKUSAGE table to examine data distribution across slices — Option C is correct because the SVV_DISKUSAGE table provides per-slice data distribution information, allowing you to identify skew by comparing the number of blocks allocated to each slice for a given table. In Amazon Redshift, data is distributed across slices based on the distribution key, and significant variation in block counts across slices indicates distribution skew, which can cause query performance degradation due to uneven workload distribution.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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