The answer is to compare row counts and checksums on both databases. This is correct because AWS DMS includes a built-in data validation feature that automatically compares row counts and checksums between the source and target, ensuring every record was migrated accurately without corruption or loss. For a 500 GB Oracle to Amazon RDS for Oracle migration, this method provides comprehensive verification, unlike querying random rows which misses gaps, or using CloudWatch which monitors performance, not data integrity. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding of DMS validation capabilities versus unrelated services like S3 inventory or CloudWatch—a common trap is assuming monitoring tools can validate data. Remember the memory tip: “Count and check, don’t just peek”—row counts catch missing rows, checksums catch altered data, together they validate consistency after migration.
DBS-C01 Deployment and Migration Practice Question
This DBS-C01 practice question tests your understanding of deployment and migration. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is migrating a 500 GB Oracle database to Amazon RDS for Oracle. They need to validate the migration and ensure data consistency. Which TWO methods should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS DMS data validation feature.
Options B and D are correct. AWS DMS provides data validation, and comparing row counts and checksums can verify consistency. Option A is wrong because CloudWatch does not validate data. Option C is wrong because querying random rows is not comprehensive. Option E is wrong because S3 inventory is unrelated to database consistency.
Key principle: ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Amazon S3 inventory reports on the exported data.
Why it's wrong here
S3 inventory is for object storage, not database consistency.
✓
Use AWS DMS data validation feature.
Why this is correct
DMS validation compares source and target data automatically.
Related concept
Standard ACLs match source addresses.
✗
Run random SELECT queries on a subset of tables.
Why it's wrong here
Random sampling does not guarantee full consistency.
✓
Compare row counts and checksums on both databases.
Why this is correct
Row counts and checksums provide a reliable comparison.
Related concept
Standard ACLs match source addresses.
✗
Use Amazon CloudWatch Logs to compare database logs.
Why it's wrong here
CloudWatch logs do not provide data validation.
Common exam traps
Common exam trap: ACLs stop at the first match
ACLs are processed top to bottom. The first matching entry wins, and an implicit deny usually exists at the end.
Detailed technical explanation
How to think about this question
ACL questions test precision: source, destination, protocol, port and direction. A generally correct ACL can still fail if it is applied on the wrong interface or in the wrong direction.
KKey Concepts to Remember
Standard ACLs match source addresses.
Extended ACLs can match source, destination, protocol and ports.
The first matching ACL entry is used.
There is usually an implicit deny at the end.
TExam Day Tips
→Check inbound versus outbound direction.
→Read the ACL from top to bottom.
→Look for a broader permit or deny above the intended line.
Key takeaway
ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related DBS-C01 ACL questions on filtering logic and placement.
Deployment and Migration — This question tests Deployment and Migration — Standard ACLs match source addresses..
What is the correct answer to this question?
The correct answer is: Use AWS DMS data validation feature. — Options B and D are correct. AWS DMS provides data validation, and comparing row counts and checksums can verify consistency. Option A is wrong because CloudWatch does not validate data. Option C is wrong because querying random rows is not comprehensive. Option E is wrong because S3 inventory is unrelated to database consistency.
What should I do if I get this DBS-C01 question wrong?
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related DBS-C01 ACL questions on filtering logic and placement.
What is the key concept behind this question?
Standard ACLs match source addresses.
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Question Discussion
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