DEA-C01 Data Operations and Support Practice Question
A company uses AWS Glue to run ETL jobs on a schedule. Recently, a job failed with the error: 'AnalysisException: cannot resolve '`column_name`' given input columns: ...'. The job reads from an Amazon S3 source that has a schema defined in the AWS Glue Data Catalog. What is the MOST likely cause?
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 schema of the source data has changed and is not reflected in the Data Catalog.
The error 'cannot resolve column_name' indicates that the Spark SQL query is trying to reference a column that does not exist in the schema provided by the AWS Glue Data Catalog. This typically happens when the source data schema has changed (e.g., column renamed or dropped) but the Data Catalog schema is not updated accordingly. Option B is incorrect because a corrupted file would cause a read or parse error, not a schema resolution error. Option C is incorrect because an IAM permissions issue would result in an AccessDenied error. Option D is incorrect because a data type mismatch would cause a type casting error, not a 'cannot resolve' error which is about column names.
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 schema of the source data has changed and is not reflected in the Data Catalog.
Why this is correct
Schema evolution without updating catalog causes column resolution errors.
- ✗
The source data file is corrupted and cannot be parsed.
Why it's wrong here
Corrupted files cause parse errors, not schema resolution errors.
- ✗
The IAM role associated with the Glue job does not have permissions to read the S3 bucket.
Why it's wrong here
Permission errors would be AccessDenied, not AnalysisException.
- ✗
The data type of the column in the source does not match the Data Catalog definition.
Why it's wrong here
Data type mismatches cause type errors, not 'cannot resolve' errors.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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