DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to transform data from Amazon S3. The source data is in CSV format with inconsistent date formats across files (e.g., 'MM/DD/YYYY' and 'YYYY-MM-DD'). The engineer needs to standardize all dates to 'YYYY-MM-DD' format in the output. Which AWS Glue transform should the engineer use to achieve this?
⚠ Common exam trap
The trap here is thinking that `ApplyMapping` can handle date format conversion, when it only handles type casting and renaming.
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
✓
Map
The `Map` transform in AWS Glue enables custom record-level transformations. By writing a Python function that parses various date formats and outputs a standardized string, the engineer can handle inconsistent date formats. This approach is flexible and can be applied across all records, ensuring uniform output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ApplyMapping
Why it's wrong here
`ApplyMapping` is used to rename fields, change data types, or drop fields. It does not provide functionality to parse or reformat date strings. While it can cast a string to a date type, it does not handle multiple inconsistent formats. The engineer needs a transform that can apply custom logic to parse and convert dates.
- ✗
Filter
Why it's wrong here
`Filter` is used to remove records based on a condition. It does not modify data values. The requirement is to transform date formats, not to filter records. Using `Filter` would not change the date representation and would leave the data inconsistent.
- ✗
ResolveChoice
Why it's wrong here
`ResolveChoice` is used to handle columns with mixed data types by choosing one type or casting. It does not perform string parsing or date format conversion. While it could resolve type conflicts, it does not address the need to reformat date strings from different patterns into a single standard.
- ✓
Map
Why this is correct
The `Map` transform allows applying a custom function to each record in a DynamicFrame. The engineer can write a Python function that attempts to parse the date string using multiple formats (e.g., using `datetime.strptime` with different format strings) and then outputs the standardized 'YYYY-MM-DD' string. This provides the flexibility needed to handle inconsistent date formats across files.
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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