DBS-C01 Management and Operations Practice Question
A company is migrating an on-premises Oracle database to Amazon Aurora PostgreSQL. The database has several large tables with frequent INSERT and UPDATE operations. Which TWO actions should be taken to optimize performance after migration?
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
✓
Enable autovacuum and configure it to run more frequently on the large tables.
Options A and B are correct. Enabling autovacuum and tuning it to run more frequently on large tables is essential in PostgreSQL to reclaim storage and prevent transaction ID wraparound, which is critical after a migration with high DML. Using Aurora PostgreSQL integration with Amazon S3 for bulk data loading is efficient because it leverages parallel processing and avoids network overhead. Option C is incorrect because setting synchronous_commit to ON reduces performance for write-heavy workloads by waiting for disk writes; OFF or REMOTE_WRITE is recommended. Option D is incorrect because RDS Proxy helps with connection management but does not directly optimize DML performance for large tables. Option E is incorrect because while partitioning can help query performance, it is not a direct optimization for INSERT/UPDATE operations and may add complexity; Aurora's storage layer already handles large tables 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 autovacuum and configure it to run more frequently on the large tables.
Why this is correct
Autovacuum prevents bloat from frequent updates, maintaining query performance.
- ✓
Use the Aurora PostgreSQL integration with Amazon S3 for bulk data loading.
Why this is correct
The S3 integration allows fast parallel loading, improving INSERT performance for large datasets.
- ✗
Set synchronous_commit to ON to ensure data durability.
Why it's wrong here
Synchronous commit ensures durability but reduces write performance; it is not an optimization strategy.
- ✗
Deploy an RDS Proxy in front of the Aurora cluster to reduce connection overhead.
Why it's wrong here
RDS Proxy helps with connection management but does not directly optimize DML performance.
- ✗
Partition the large tables by date to improve query performance.
Why it's wrong here
Partitioning can help with data management but is not specifically for DML performance on large tables.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
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This DBS-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 DBS-C01 exam.