- A
AWS Glue Data Catalog
Data Catalog stores schema and can be updated as schema evolves.
- B
AWS Glue DynamicFrame
DynamicFrame can handle schema changes by allowing optional fields.
- C
AWS Lake Formation
Why wrong: Lake Formation is for access control and data lake management, not schema evolution.
- D
Amazon Athena
Why wrong: Athena queries data but does not manage schema evolution.
- E
Amazon S3 object tags
Why wrong: Object tags are metadata, not for schema evolution.
Quick Answer
The answer is AWS Glue DynamicFrame and the AWS Glue Data Catalog. These two features work together to handle schema evolution by automatically accommodating new columns added over time: DynamicFrame uses a schema-on-read approach that treats new fields as optional, allowing transformations to proceed without breaking when the source data structure changes, while the Data Catalog stores the evolving schema and can be updated to reflect new columns through crawlers or manual edits. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how Glue’s ETL engine differs from rigid, schema-on-write systems—a common trap is confusing Lake Formation’s security permissions or Athena’s query capabilities with schema management. Remember the memory tip: “DynamicFrame bends, Data Catalog tracks” to recall that DynamicFrame flexibly adapts to new fields at runtime, and the Catalog keeps the schema history.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 data engineer is building a data pipeline using AWS Glue. The pipeline reads data from Amazon S3, transforms it, and writes it back to S3 in a different format. The engineer needs to handle schema evolution (new columns added over time). Which TWO features of AWS Glue can help manage schema evolution?
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
AWS Glue Data Catalog
Options B and D are correct. The Glue Data Catalog can store schema and update it as new columns are added. DynamicFrame in Glue ETL can handle schema changes automatically by allowing optional fields. Option A is wrong because AWS Lake Formation is for data lake security, not schema evolution. Option C is wrong because Amazon Athena is a query engine, not a schema evolution tool. Option E is wrong because S3 object tags are not for schema management.
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.
- ✓
AWS Glue Data Catalog
Why this is correct
Data Catalog stores schema and can be updated as schema evolves.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
AWS Glue DynamicFrame
Why this is correct
DynamicFrame can handle schema changes by allowing optional fields.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Lake Formation
Why it's wrong here
Lake Formation is for access control and data lake management, not schema evolution.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data but does not manage schema evolution.
- ✗
Amazon S3 object tags
Why it's wrong here
Object tags are metadata, not for schema evolution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
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.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Glue Data Catalog — Options B and D are correct. The Glue Data Catalog can store schema and update it as new columns are added. DynamicFrame in Glue ETL can handle schema changes automatically by allowing optional fields. Option A is wrong because AWS Lake Formation is for data lake security, not schema evolution. Option C is wrong because Amazon Athena is a query engine, not a schema evolution tool. Option E is wrong because S3 object tags are not for schema management.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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