- A
Manually update the table schema in the Glue Data Catalog before each run
Why wrong: Manual updates are error-prone and not scalable.
- B
Use Spark SQL with a static schema definition in the script
Why wrong: Static schema will fail if the JSON structure changes unexpectedly.
- C
Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true
This allows Glue DynamicFrame to merge schema variations automatically.
- D
Enable AWS Glue Schema Registry and define a schema version
Why wrong: Schema Registry enforces schema, but does not automatically handle evolution; manual versioning needed.
Question 681 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 AWS Glue to process JSON logs from S3. The logs have a nested structure and the schema evolves over time. The data engineer needs to ensure the Glue job can handle schema changes without failing. Which configuration should be used?
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
✓
Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true
Option C is correct because setting '--enable-glue-datacatalog' allows the Glue job to use the Data Catalog as the metastore, and '--mergeDynamicColumns' (or the equivalent '--enable-schema-evolution' in newer Glue versions) instructs the job to dynamically merge new columns from the evolving JSON schema into the existing table schema during runtime, preventing job failures due to schema mismatches. This is specifically designed for nested, schema-evolving data like JSON logs, as it automatically reconciles differences between the source data and the catalog definition.
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.
- ✗
Manually update the table schema in the Glue Data Catalog before each run
Why it's wrong here
Manual updates are error-prone and not scalable.
- ✗
Use Spark SQL with a static schema definition in the script
Why it's wrong here
Static schema will fail if the JSON structure changes unexpectedly.
- ✓
Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true
Why this is correct
This allows Glue DynamicFrame to merge schema variations automatically.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable AWS Glue Schema Registry and define a schema version
Why it's wrong here
Schema Registry enforces schema, but does not automatically handle evolution; manual versioning needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the AWS Glue Schema Registry (which enforces schema compatibility and versioning) with the schema evolution capabilities of the Glue DynamicFrame, leading them to choose Option D even though it would reject schema changes rather than adapt to them.
Detailed technical explanation
How to think about this question
Under the hood, when '--mergeDynamicColumns' is enabled, the Glue DynamicFrame uses a 'schema inference' pass on the data and then merges any new columns into the existing Data Catalog table schema via a 'schema update' operation, which is equivalent to running an ALTER TABLE ADD COLUMNS command but automatically during job execution. This is particularly useful for nested JSON where new subfields appear in arrays or structs, as the merge logic recursively handles nested structures. A real-world scenario is ingesting IoT device logs where each device may send different fields over time; without this setting, the job would fail on the first record with an unexpected column.
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: Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true — Option C is correct because setting '--enable-glue-datacatalog' allows the Glue job to use the Data Catalog as the metastore, and '--mergeDynamicColumns' (or the equivalent '--enable-schema-evolution' in newer Glue versions) instructs the job to dynamically merge new columns from the evolving JSON schema into the existing table schema during runtime, preventing job failures due to schema mismatches. This is specifically designed for nested, schema-evolving data like JSON logs, as it automatically reconciles differences between the source data and the catalog definition.
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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