DEA-C01 Data Operations and Support Practice Question
A data engineer manages an AWS Glue ETL job that processes JSON files from an S3 bucket and writes Parquet to another bucket. The job uses a Glue DynamicFrame with a specified schema. During execution, the job fails with the error: 'AnalysisException: cannot resolve column 'transaction_id' given input columns: [txn_id, amount, timestamp]'. The source data has a column named 'txn_id', but the Glue job's script references 'transaction_id'. The job's catalog table for the source points to the correct S3 location and has the correct schema. What is the most likely cause of this error?
⚠ Common exam trap
The trap here is assuming that the Glue Data Catalog schema is always used by the job, but the script may define its own schema or mappings that override the catalog.
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
✓
The Glue job's script uses a hardcoded schema that does not match the actual data columns.
The error occurs because the Glue script references a column that does not exist in the source data. Even though the Glue Data Catalog is correct, the script may have been written with a hardcoded schema or mapping that expects 'transaction_id' instead of 'txn_id'. To resolve, the engineer should update the script to use the correct column name or apply a mapping to rename 'txn_id' to 'transaction_id'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Glue job's IAM role lacks permission to read the S3 bucket, resulting in an empty DynamicFrame.
Why it's wrong here
An IAM permission issue would typically result in an access denied error or an empty dataset, not a column resolution error. The error message specifically states that the column 'transaction_id' cannot be resolved given the existing columns, indicating that data was read successfully but the column names do not match the script's expectations.
- ✗
The Glue Data Catalog table for the source has an incorrect schema definition.
Why it's wrong here
The scenario states that the catalog table has the correct schema and points to the correct S3 location. Therefore, the catalog is not the source of the error. The error arises during job execution when the script attempts to reference a column that does not exist in the DynamicFrame, which is separate from the catalog metadata.
- ✓
The Glue job's script uses a hardcoded schema that does not match the actual data columns.
Why this is correct
The error indicates that the column 'transaction_id' is not present in the input data, while 'txn_id' exists. If the script applies a hardcoded schema or a mapping that expects 'transaction_id', the Spark engine cannot resolve it. This is a common issue when the script is written with a static schema that diverges from the actual source structure, even if the catalog is correct.
- ✗
The S3 bucket contains files with inconsistent schemas, causing Glue to infer a different column name.
Why it's wrong here
While schema inconsistencies can cause issues, the error explicitly shows that the input columns include 'txn_id', not 'transaction_id'. This suggests a consistent schema across files, and the problem is the script's expectation of a different column name. If there were inconsistencies, the error would likely mention multiple conflicting schemas or type mismatches.
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
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