DEA-C01 practice question: Monitoring and logging pipelines (1 of 3)
Data Operations and Support A free question from our original DEA-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A ticketing company's Amazon Redshift cluster uses manual WLM, with one queue for ETL and one for analysts. Some ad hoc analyst queries run for hours and scan billions of rows, and the nightly ETL slows down while they run.
The data team wants long-running analyst queries recorded for review, and any analyst query that scans more than 5 billion rows stopped automatically. ETL queries must not be affected, and the team does not want to write scripts.
Which solution meets these requirements?
Choose one answer.
AA. Schedule a Lambda function every five minutes that queries STV_RECENTS and cancels any analyst query that has run for more than one hour.
BB. Add query monitoring rules to the analyst queue, with a log action for long run times and an abort action for queries scanning over 5 billion rows.
CC. Set the statement_timeout parameter in the cluster's parameter group, so that every query stops after it has run for one hour.
DD. Turn on concurrency scaling for the analyst queue, so that the long analyst queries run on extra capacity and leave the ETL queue alone.
Show the answer and explanation
Answer: B
A. Wrong: This is the script the team wants to avoid, and it judges queries by run time rather than by the rows they scan. A large scan could also do its damage between two polls.
B. Right: Correct. Query monitoring rules belong to one WLM queue, so ETL queries are not affected. A log action records the query in STL_WLM_RULE_ACTION, and an abort action on the scan_row_count metric cancels the large scans without any script. Learn more: WLM query monitoring rules (opens in a new tab)
C. Wrong: A cluster-wide timeout also stops long ETL statements, which must not be affected. It limits run time only, so a short query that scans billions of rows still runs.
D. Wrong: Concurrency scaling adds capacity for queued queries, but it neither records nor stops the expensive ones. The large scans still run and add cost.
Explanation
Redshift query monitoring rules watch the queries in one WLM queue and act when their metrics cross a threshold. A log action keeps a record for review, and an abort action on rows scanned stops the expensive queries, with no effect on other queues. Polling scripts, cluster-wide timeouts and extra capacity miss the scope, the metric or the no-script requirement.
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