DEA-C01 Data Operations and Support Practice Question

A data engineer manages an AWS Glue ETL job that reads CSV files from Amazon S3 and writes Parquet to another S3 prefix. The job recently started failing with the error 'AnalysisException: Unable to infer schema for CSV.' The engineer confirms the S3 path contains files and the IAM role has s3:GetObject permissions. The job's script uses glueContext.create_dynamic_frame.from_catalog with a database and table name. What is the MOST likely cause?

⚠ Common exam trap

The trap here is assuming that direct S3 file access or compression causes the schema inference error, when the job is actually reading metadata from the Data 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 AWS Glue Data Catalog table's SerDe parameters are misconfigured, so the crawler did not correctly identify the CSV delimiter or header.

When an AWS Glue job uses create_dynamic_frame.from_catalog, it depends entirely on the AWS Glue Data Catalog table definition to determine the schema. If the table's SerDe parameters are incorrect—such as a wrong delimiter, missing header, or incorrect classification—the job cannot infer the schema and throws the AnalysisException. Verifying and repairing the catalog table resolves the failure.

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 AWS Glue job's bookmarks are enabled, causing it to skip files that were already processed and leaving no data to infer schema from.
Why it's wrong here
Job bookmarks track previously processed data, but they do not cause schema inference errors. If bookmarks skipped all files, the job would complete with no new data, not fail with an AnalysisException about schema. The error indicates a metadata problem, not bookmark behavior.
✗
The IAM role lacks s3:ListBucket permission on the source bucket, so the job cannot list the CSV files.
Why it's wrong here
The scenario states the IAM role has s3:GetObject permissions, but even if s3:ListBucket were missing, the error would typically be an access denied or file not found, not a schema inference failure. Since the job reads via the Data Catalog, listing the bucket directly is not required for schema resolution.
✗
The CSV files are compressed with gzip, and AWS Glue cannot infer schema from compressed files.
Why it's wrong here
AWS Glue supports reading gzip-compressed CSV files and can infer schema from them. Compression does not inherently cause the 'Unable to infer schema' error; the issue lies in the catalog table metadata or file format detection, not the compression codec.
✓
The AWS Glue Data Catalog table's SerDe parameters are misconfigured, so the crawler did not correctly identify the CSV delimiter or header.
Why this is correct
The error 'Unable to infer schema for CSV' occurs when the Glue job reads from the Data Catalog and the table metadata lacks a valid SerDe or has incorrect parameters such as separatorType or header. The job relies on the catalog table definition, not direct file inspection, so misconfigured metadata prevents schema inference.

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

Go deeper

DEA-C01 Data Operations and Support Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question