- A
Schedule a Glue crawler to run after each ETL job to update the Data Catalog.
Why wrong: This adds extra time and cost, and does not adapt the job itself.
- B
Set the job to use schema-on-read by storing data in Parquet format.
Why wrong: Schema-on-read allows querying without predefined schema but does not adapt the ETL transformation.
- C
Enable the 'Update schema' option in the Glue job's output target configuration.
This option automatically adds new columns to the target table.
- D
Use Glue's partition indexes to automatically detect new columns.
Why wrong: Partition indexes are for partition pruning, not schema evolution.
Question 82 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 uses Amazon S3 to store raw data and AWS Glue to run ETL jobs that transform the data into analytics-ready tables. The Glue job reads from a source with a schema that changes frequently (new columns added). The engineer wants the Glue job to automatically adapt to schema changes without manual intervention. Which configuration should the engineer 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
✓
Enable the 'Update schema' option in the Glue job's output target configuration.
Option C is correct because enabling the 'Update schema' option in the Glue job's output target configuration allows the job to automatically add new columns to the target table in the Data Catalog when the source schema changes. This setting directly addresses the requirement for automatic adaptation to schema changes without manual intervention, as it updates the table definition during the ETL job run.
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.
- ✗
Schedule a Glue crawler to run after each ETL job to update the Data Catalog.
Why it's wrong here
This adds extra time and cost, and does not adapt the job itself.
- ✗
Set the job to use schema-on-read by storing data in Parquet format.
Why it's wrong here
Schema-on-read allows querying without predefined schema but does not adapt the ETL transformation.
- ✓
Enable the 'Update schema' option in the Glue job's output target configuration.
Why this is correct
This option automatically adds new columns to the target table.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Glue's partition indexes to automatically detect new columns.
Why it's wrong here
Partition indexes are for partition pruning, not schema evolution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse schema-on-read (Parquet's flexibility) with automatic schema evolution in the Data Catalog, leading them to choose Option B, but schema-on-read does not update the target table's metadata, which is required for downstream analytics tools to query the new columns.
Detailed technical explanation
How to think about this question
The 'Update schema' option works by comparing the schema of the output DataFrame with the existing table schema in the Data Catalog during the job run. If new columns are detected, it issues an ALTER TABLE ADD COLUMNS DDL statement to the underlying Hive metastore (AWS Glue Data Catalog), ensuring the target table schema evolves without requiring a separate crawler or manual DDL. This is particularly useful in streaming or incremental ETL scenarios where source schemas evolve frequently, such as IoT sensor data adding new metrics over time.
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable the 'Update schema' option in the Glue job's output target configuration. — Option C is correct because enabling the 'Update schema' option in the Glue job's output target configuration allows the job to automatically add new columns to the target table in the Data Catalog when the source schema changes. This setting directly addresses the requirement for automatic adaptation to schema changes without manual intervention, as it updates the table definition during the ETL job run.
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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Last reviewed: Jul 4, 2026
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