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DBS-C01 · Question #311

A company is planning to migrate a 40 TB Oracle database to an Amazon Aurora PostgreSQL DB cluster by using a single AWS Database Migration Service (AWS DMS) task within a single replication…

The correct answer is B. Use a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a D. Use parallel load with different data boundaries for larger tables. https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Tasks.CustomizingTasks.TaskSetting

Submitted by obi.ng· Mar 6, 2026Deployment and Migration

Question

A company is planning to migrate a 40 TB Oracle database to an Amazon Aurora PostgreSQL DB cluster by using a single AWS Database Migration Service (AWS DMS) task within a single replication instance. During early testing, AWS DMS is not scaling to the company's needs. Full load and change data capture (CDC) are taking days to complete. The source database server and the target DB cluster have enough network bandwidth and CPU bandwidth for the additional workload. The replication instance has enough resources to support the replication. A database specialist needs to improve database performance, reduce data migration time, and create multiple DMS tasks. Which combination of changes will meet these requirements? (Choose two.)

Options

  • AIncrease the value of the ParallelLoadThreads parameter in the DMS task settings for the tables.
  • BUse a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a
  • CUse a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a
  • DUse parallel load with different data boundaries for larger tables.
  • ERun the DMS tasks on a larger instance class. Increase local storage on the instance.

How the community answered

(32 responses)
  • A
    13% (4)
  • B
    59% (19)
  • C
    22% (7)
  • E
    6% (2)

Explanation

https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Tasks.CustomizingTasks.TaskSetting

Topics

#AWS DMS performance#large-scale migration#full load optimization#CDC optimization

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