DEA-C01 Data Ingestion and Transformation Practice Question
Exhibit
CREATE EXTERNAL TABLE IF NOT EXISTS my_database.sales ( order_id INT, customer_name STRING, product STRING, amount DECIMAL(10,2), order_date STRING ) ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' WITH SERDEPROPERTIES ( 'field.delim' = ',' ) LOCATION 's3://my-bucket/sales/'
Refer to the exhibit. A data engineer runs this AWS Glue Data Catalog DDL statement to create a table. The CSV files in 's3://my-bucket/sales/' use a pipe delimiter (|) instead of a comma. What change is needed to correctly read the data?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that changing the LOCATION or adding partition projection will fix parsing issues, when in fact the core problem is the SerDe delimiter property not matching the actual file format.
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 'field.delim' property to '|'.
The AWS Glue Data Catalog DDL statement uses the default 'field.delim' property, which expects comma-separated values. Since the CSV files use a pipe delimiter (|), the table will not parse rows correctly. Setting 'field.delim' to '|' in the SerDe properties tells the Hive-compatible SerDe to split on pipes instead of commas, enabling correct data ingestion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the 'field.delim' property to '|'.
Why this is correct
Setting `field.delim` to `|` overrides the default comma separator, so the Glue SerDe splits each CSV row on the pipe character rather than commas. This satisfies the stem's constraint that the source files in `s3://my-bucket/sales/` are pipe-delimited, allowing columns to be parsed correctly.
- ✗
Change the LOCATION to read from a subfolder.
Why it's wrong here
The LOCATION path is already correct; the failure stems from the SerDe parsing with a comma delimiter, so altering the folder does not change how fields are split. Changing LOCATION is tempting when files sit in subfolders, but the delimiter mismatch remains regardless of path.
- ✗
Add a partition projection configuration.
Why it's wrong here
Partition projection speeds up query planning by generating partition metadata; it does not alter the SerDe's delimiter, so fields still parse incorrectly. It is tempting for large partitioned datasets, but the actual defect is comma versus pipe delimiting, which projection cannot fix.
- ✗
Run a crawler to detect the schema automatically.
Why it's wrong here
A crawler infers schema and classification but cannot override the explicit SerDe properties already defined in the DDL, so it would not correct the comma delimiter. Crawlers suit discovering unknown schemas; here the schema is known and only the field delimiter needs changing.
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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