DEA-C01 Data Operations and Support Practice Question
A team uses Amazon Redshift for analytics. They notice that some queries are slow and the system shows high disk usage. The team wants to improve query performance without adding more nodes. Which action should they take first?
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
✓
Run the VACUUM and ANALYZE commands on the tables.
VACUUM and ANALYZE are the first actions to take because they reclaim disk space from deleted rows and update table statistics, which can significantly improve query performance and reduce disk usage. Option B is incorrect because enabling compression on all columns is not a first step; compression is typically set during table creation and may not address current disk usage issues. Option C is incorrect: redistributing tables by changing the distribution key requires recreating the table, which is an invasive operation and not the first troubleshooting step. Option D is incorrect: modifying the WLM queue affects concurrency management, not disk usage or query performance directly related to high disk usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Run the VACUUM and ANALYZE commands on the tables.
Why this is correct
VACUUM reclaims space from deleted rows and re-sorts data, while ANALYZE updates table statistics used by the query planner. Both address the high disk usage and slow queries without adding nodes, making them the correct first action.
- ✗
Enable compression on all columns.
Why it's wrong here
Compression reduces storage footprint and I/O, but the stem's slow queries point to skew or inefficient scans, which compression alone will not resolve. It is tempting because columnar compression genuinely cuts disk usage and is a standard Redshift tuning step, yet it would be the right first action when tables hold large, repetitive text columns and storage, not query latency, is the binding constraint.
- ✗
Redistribute the tables by changing the distribution key to a column with high cardinality.
Why it's wrong here
Changing the distribution key to a high-cardinality column can increase cross-node data movement during joins, worsening the slow queries rather than relieving disk pressure. Redistribution is tempting because it eliminates skew, and it would be correct when a single node holds disproportionate rows because the current key has low cardinality.
- ✗
Modify the workload management (WLM) queue to increase concurrency.
Why it's wrong here
Raising WLM concurrency lets more queries run simultaneously, but with high disk usage this multiplies competing scans and degrades throughput. WLM tuning is tempting because it addresses queue waits, and it would be the right choice when queries sit idle in a queue while cluster CPU and memory remain underutilised.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.