DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS DMS to migrate a 2 TB Oracle database to Amazon RDS for PostgreSQL. The migration is taking longer than expected. The task status shows 'Full load in progress' with a low 'Table throughput (rows/s)'. Which action would MOST improve throughput?
⚠ Common exam trap
Candidates often confuse 'parallel tasks' with 'Multi-AZ' or 'target table preparation mode', assuming that high availability or skipping table preparation will speed up data transfer, when in fact only increasing parallelism directly addresses low row throughput during full load.
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
✓
Increase the number of parallel tasks in the DMS task settings
The low 'Table throughput (rows/s)' during the full load phase indicates that the DMS task is not processing tables with enough parallelism. Increasing the number of parallel tasks in the DMS task settings allows the replication instance to load multiple tables concurrently, which directly improves throughput by utilizing available CPU and memory resources more efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Multi-AZ on the DMS replication instance
Why it's wrong here
Multi-AZ provides failover but does not improve throughput.
- ✗
Change the target table preparation mode to 'Do nothing'
Why it's wrong here
This avoids truncation but does not affect load speed.
- ✓
Increase the number of parallel tasks in the DMS task settings
Why this is correct
Parallel tasks allow concurrent loading of tables, increasing throughput.
- ✗
Increase the number of shards in the source database
Why it's wrong here
Sharding is not applicable to Oracle; DMS handles sharding internally.
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