Quick Answer
The answer is AWS DMS data validation, the correct tool to validate data consistency after migrating MongoDB to DocumentDB. AWS DMS performs row-level checksum-based validation by computing and comparing checksums between the source MongoDB and target DocumentDB during and after the full load or ongoing replication, ensuring every document matches without requiring custom scripts. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your understanding of AWS-native migration integrity tools versus manual methods like dbHash or mongoexport, which are common traps because they lack DMS’s automated, real-time comparison. Remember that DMS validation works at the row level for DocumentDB’s MongoDB compatibility, not just at the collection level. Memory tip: “DMS Does Matching Sums” — DMS validates data by matching checksums, making it the go-to for post-migration consistency checks.
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 an on-premises MongoDB database to Amazon DocumentDB (with MongoDB compatibility). They want to validate that the data is consistent after migration. Which tool should they use?
mongoexport and mongoimport
Why wrong: These are for data transfer, not validation.
dbHash command on both databases
Why wrong: dbHash checks consistency of a single instance, not cross-database.
DocumentDB native consistency check tool
Why wrong: DocumentDB does not have a native consistency check tool for migration.
AWS DMS data validation
DMS can validate data between source and target.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS DMS data validation
AWS DMS data validation is the correct tool because it provides built-in, row-level checksum-based validation that compares source and target data after a full load or ongoing replication. For DocumentDB migrations, DMS can compute and compare checksums on the fly, ensuring consistency without requiring manual scripting or external tools. This is the recommended AWS approach for validating data integrity during and after a migration to DocumentDB.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
mongoexport and mongoimport
Why it's wrong here
These are for data transfer, not validation.
dbHash command on both databases
Why it's wrong here
dbHash checks consistency of a single instance, not cross-database.
DocumentDB native consistency check tool
Why it's wrong here
DocumentDB does not have a native consistency check tool for migration.
AWS DMS data validation
Why this is correct
DMS can validate data between source and target.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates assume a native MongoDB command like dbHash or a generic export/import tool can validate consistency across different database engines, but DocumentDB does not support dbHash and AWS DMS is the only service-integrated validation method for cross-engine migrations.
Detailed technical explanation
AWS DMS data validation works by computing a checksum for each row (or document) using a hash function (e.g., MD5) on the source and target, then comparing results. For DocumentDB, DMS uses the MongoDB wire protocol to read documents and compute checksums, handling BSON types and document ordering carefully. In real-world scenarios, validation can catch silent data corruption or transformation errors that occur during migration, especially when schema conversion or data type mapping is involved.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Deployment and Migration — This question tests Deployment and Migration — Read the scenario before looking for a memorised answer..
The correct answer is: AWS DMS data validation — AWS DMS data validation is the correct tool because it provides built-in, row-level checksum-based validation that compares source and target data after a full load or ongoing replication. For DocumentDB migrations, DMS can compute and compare checksums on the fly, ensuring consistency without requiring manual scripting or external tools. This is the recommended AWS approach for validating data integrity during and after a migration to DocumentDB.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 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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