- A
Compare row counts between source and target tables.
Why wrong: Row count comparison does not verify data content.
- B
Use AWS DMS data validation feature.
DMS data validation compares source and target data using table-level checksums.
- C
Run random SELECT queries on a subset of rows to compare values.
Why wrong: Random sampling does not guarantee full data consistency.
- D
Compute MD5 checksums of the entire dataset in both databases.
Why wrong: Computing checksums on large datasets is resource-intensive and impractical.
Question 923 of 1,730
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 3 TB MySQL database to Amazon Aurora MySQL. They need to validate data consistency after migration. Which approach should they use?
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
✓
Use AWS DMS data validation feature.
AWS DMS data validation feature is the correct approach because it automatically compares source and target records by computing checksums at the table level, ensuring end-to-end consistency without manual intervention. For a 3 TB migration, this built-in feature handles large-scale validation efficiently by validating ongoing replication and full-load data, which is critical for MySQL to Aurora MySQL migrations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compare row counts between source and target tables.
Why it's wrong here
Row count comparison does not verify data content.
- ✓
Use AWS DMS data validation feature.
Why this is correct
DMS data validation compares source and target data using table-level checksums.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Run random SELECT queries on a subset of rows to compare values.
Why it's wrong here
Random sampling does not guarantee full data consistency.
- ✗
Compute MD5 checksums of the entire dataset in both databases.
Why it's wrong here
Computing checksums on large datasets is resource-intensive and impractical.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume row count comparison (Option A) is sufficient for data consistency, but the exam tests the understanding that only a row-level checksum comparison (like DMS validation) can guarantee data integrity in large-scale migrations.
Detailed technical explanation
How to think about this question
AWS DMS data validation works by computing a checksum for each row using a hash of all columns, then comparing these checksums between source and target during ongoing replication. It can detect mismatches at the row level and report them in the console, supporting both full-load and CDC phases. In a real-world scenario, if the migration involves schema transformations or data type conversions, DMS validation can catch subtle issues like truncation or rounding errors that row counts or random queries would miss.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Deployment and Migration — This question tests Deployment and Migration — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS DMS data validation feature. — AWS DMS data validation feature is the correct approach because it automatically compares source and target records by computing checksums at the table level, ensuring end-to-end consistency without manual intervention. For a 3 TB migration, this built-in feature handles large-scale validation efficiently by validating ongoing replication and full-load data, which is critical for MySQL to Aurora MySQL migrations.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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