The answer is AWS Glue, which is the correct choice because it is a fully managed, serverless extract, transform, and load (ETL) service designed specifically for transforming data during migration to Redshift using Glue. When moving a 5 TB Oracle data warehouse, Glue can read the source data, apply necessary transformations—such as schema mapping, data type conversions, and cleansing—and then load the transformed dataset directly into Redshift tables, all without provisioning any underlying infrastructure. On the AWS Certified Database Specialty DBS-C01 exam, this question tests your ability to distinguish between services that handle transformation versus those focused on minimal-change replication or querying. A common trap is confusing AWS DMS, which excels at near-zero-downtime migration but lacks robust transformation capabilities, with Glue’s purpose-built ETL engine. Remember the memory tip: “Glue transforms, DMS transports” to quickly recall that Glue is your go-to for reshaping data during a Redshift 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 planning to migrate a 5 TB Oracle data warehouse to Amazon Redshift. They need to transform the data during migration. Which AWS service should they use to perform the transformation?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Glue to perform extract, transform, and load (ETL) jobs.
Option D is correct because AWS Glue is a serverless ETL service that can perform transformations. Option A is wrong because AWS DMS is for database migration with minimal transformation. Option B is wrong because Kinesis is for real-time streaming data. Option C is wrong because Athena is a query service, not an ETL tool.
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.
✗
Amazon Kinesis Data Analytics for real-time transformation.
Why it's wrong here
Kinesis is for streaming data, not for batch transformation of large datasets.
✗
Amazon Athena to query the source data and write results to Redshift.
Why it's wrong here
Athena is for querying data in S3, not for ETL transformations.
✓
AWS Glue to perform extract, transform, and load (ETL) jobs.
Why this is correct
Glue is a fully managed ETL service suitable for transforming data before loading into Redshift.
Related concept
Standard ACLs match source addresses.
✗
AWS Database Migration Service (DMS) with transformation rules.
Why it's wrong here
DMS is primarily for replication and offers limited transformation capabilities.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. 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. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: AWS Glue to perform extract, transform, and load (ETL) jobs. — Option D is correct because AWS Glue is a serverless ETL service that can perform transformations. Option A is wrong because AWS DMS is for database migration with minimal transformation. Option B is wrong because Kinesis is for real-time streaming data. Option C is wrong because Athena is a query service, not an ETL tool.
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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