AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company runs an Amazon Redshift cluster that serves dashboards refreshed every few seconds. Once a month the data engineering team needs to run a long-running UPDATE statement that backfills a 3 TB fact table. During the backfill, BI users must not be able to read the table until the update has finished and the transaction is committed. What is the MOST appropriate way to meet this requirement while keeping the solution simple and cost-effective?
Revoke SELECT permissions from BI user groups before the UPDATE and grant them again after the commit.
Create a new table, load the updated data, then atomically swap tables with ALTER TABLE ... RENAME.
Pause the Amazon Redshift cluster, run the UPDATE, and resume the cluster when finished.
Start a transaction, run LOCK fact_table IN ACCESS EXCLUSIVE MODE, execute the UPDATE, then commit.
Issuing the LOCK command in a transaction places a table-level lock that blocks any concurrent sessions, including the BI dashboards, from acquiring even an ACCESS SHARE lock. Using ACCESS EXCLUSIVE (or EXCLUSIVE) mode ensures no session can read or write the table until the transaction commits, after which the lock is released automatically. Revoking permissions or pausing the cluster disrupts other objects or incurs downtime, and swapping tables with renames still allows dashboards to query stale data during the load, which violates the requirement that no reads occur while the backfill is running.
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What does ACCESS EXCLUSIVE MODE in Redshift do?
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Why is pausing the Redshift cluster not a good solution for this scenario?
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How does ALTER TABLE RENAME swapping work, and why is it not appropriate here?
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What does 'LOCK IN ACCESS EXCLUSIVE MODE' do in Amazon Redshift?
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How does Amazon Redshift manage transactions and locks?
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Why is 'LOCK IN ACCESS EXCLUSIVE MODE' better than swapping tables for this scenario?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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