DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building an AWS Glue Studio visual ETL job that reads JSON files from Amazon S3, applies a transformation, and writes to Amazon Redshift. During a test run, the job fails with an error indicating that the dynamic frame could not be written because the target table schema does not match the incoming data. The engineer needs to ensure the job automatically reconciles schema differences such as missing columns and data type mismatches during the write. Which action should the engineer take?
⚠ Common exam trap
The trap here is assuming that Glue connection settings or job bookmarks control runtime schema behavior, when schema reconciliation is a property of the write node itself.
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
✓
Change the write node's 'Handling of schema differences' to 'Reconcile schema' so missing columns are added and type mismatches are cast.
The Redshift write node in AWS Glue Studio provides a 'Handling of schema differences' setting. Selecting 'Reconcile schema' lets the writer add missing columns and cast compatible types so the dynamic frame matches the target table. This is the supported mechanism for automatically aligning incoming data with an existing Redshift schema, and it resolves the described failure without manual DDL changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the job bookmarks option to 'Enable' so Glue tracks schema changes between runs and applies them to the target.
Why it's wrong here
Job bookmarks track which source data has already been processed to avoid reprocessing. They do not detect or apply schema changes to a Redshift target. Enabling bookmarks would change incremental processing behavior but leave the schema mismatch unresolved, so the write would still fail during the test run.
- ✓
Change the write node's 'Handling of schema differences' to 'Reconcile schema' so missing columns are added and type mismatches are cast.
Why this is correct
In AWS Glue Studio, the Redshift write node exposes a 'Handling of schema differences' property. Setting it to 'Reconcile schema' lets the writer add missing columns and coerce compatible data types to match the target table. This directly addresses the failure described, since the dynamic frame will be aligned to the Redshift table schema before the COPY operation is issued.
- ✗
Enable the 'Auto schema reconciliation' option in the Glue job's Redshift connection parameters.
Why it's wrong here
There is no 'Auto schema reconciliation' option in the Glue Redshift connection parameters. Glue connections for Redshift store JDBC connectivity details, not runtime schema handling behavior. Schema reconciliation is controlled at the write node or via the DynamicFrame writer, so this setting would not resolve the mismatch and the job would continue to fail against the existing Redshift table.
- ✗
Increase the number of DPUs allocated to the Glue job so the write can complete with a larger executor pool.
Why it's wrong here
DPU count affects compute capacity and parallelism, not schema compatibility. Adding workers cannot add missing columns or cast mismatched types in the Redshift target table. The job would still fail with the same schema error, only faster or with more concurrent write attempts, so scaling the cluster does not address the root cause.
Visual reference
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 |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.