The answer is to use AWS Glue Data Catalog with crawlers to automatically update the table schema. This approach is correct because Glue crawlers can infer the schema from new Parquet files as they land in S3, then update the Data Catalog table definition to reflect the evolving structure—such as added columns or changed data types—without requiring manual DDL changes. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how to decouple storage from query engines while maintaining flexibility; a common trap is to manually redefine the schema in Athena or rely on Hive-style partitions alone, which breaks when column order or types shift. The key concept here is that Glue’s schema registry handles versioning transparently, so Athena always queries the latest schema. Memory tip: think “Crawl, Catalog, Query” — the crawler updates the catalog, then Athena queries the catalog, not the raw files directly.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 designing a data lake on Amazon S3. The data is ingested from multiple sources in Parquet format, and the schema evolves over time. Which approach allows querying the data with Amazon Athena while supporting schema evolution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue Data Catalog with crawlers to automatically update the table schema.
AWS Glue Data Catalog with crawlers automatically infers and updates the table schema as new Parquet files with evolving schemas are ingested into S3. This allows Athena to query the data using the latest schema without manual intervention, making it the ideal solution for schema evolution in a data lake.
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.
✓
Use AWS Glue Data Catalog with crawlers to automatically update the table schema.
Why this is correct
Crawlers can detect schema changes and update the Data Catalog, which Athena uses.
Related concept
Read the scenario before looking for a memorised answer.
✗
Define Hive-style partitions in Athena and manually update the schema.
Why it's wrong here
Manual updates are error-prone and not scalable for evolving schemas.
✗
Use S3 Select to query the data directly without a schema.
Why it's wrong here
S3 Select does not support schema evolution; it is a simple filtering capability.
✗
Use Amazon Redshift Spectrum with external tables and update the schema manually.
Why it's wrong here
Redshift Spectrum also requires schema definition in the Data Catalog; manual updates are not efficient.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think S3 Select or Redshift Spectrum can handle schema evolution automatically, but they lack the schema inference and versioning capabilities that AWS Glue Data Catalog provides for Athena.
Detailed technical explanation
How to think about this question
AWS Glue crawlers use classifiers to infer schema from Parquet files, including nested structures and data types, and update the Data Catalog's table metadata with new columns or type changes. Athena leverages the Data Catalog's schema versioning to handle schema evolution, allowing queries to succeed even when new fields are added to Parquet files. In practice, this is critical for streaming or batch ingestion pipelines where source schemas change frequently, such as adding new sensor fields in IoT data.
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
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue Data Catalog with crawlers to automatically update the table schema. — AWS Glue Data Catalog with crawlers automatically infers and updates the table schema as new Parquet files with evolving schemas are ingested into S3. This allows Athena to query the data using the latest schema without manual intervention, making it the ideal solution for schema evolution in a data lake.
What should I do if I get this DEA-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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This DEA-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 DEA-C01 exam.
Question Discussion
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