DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue DataBrew to clean a dataset stored in Amazon S3. The dataset contains a column with inconsistent date formats (for example, '2023-01-15', '01/15/2023', and '15-Jan-2023'). The engineer needs to standardize all values to ISO 8601 format and then write the cleaned data back to S3. Which approach should the engineer use?
⚠ Common exam trap
The trap here is assuming that a custom transform is needed for mixed date formats, when DataBrew already provides a built-in date standardization transform.
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
✓
Use the 'Standardize date' transform in DataBrew, select the column, and choose the target format as ISO 8601.
AWS Glue DataBrew includes a 'Standardize date' transform that parses multiple date formats and converts them to a specified target format such as ISO 8601. This transform is designed for exactly this scenario, where a column contains mixed date representations. Using it avoids custom code and ensures consistent, repeatable cleaning across the dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the 'Standardize date' transform in DataBrew, select the column, and choose the target format as ISO 8601.
Why this is correct
AWS Glue DataBrew provides a built-in 'Standardize date' transform that can parse multiple date formats and convert them to a consistent target format such as ISO 8601. The engineer selects the column, configures the transform, and DataBrew applies it across the dataset. The cleaned data can then be written back to S3 as part of a DataBrew recipe or job.
- ✗
Use the 'Format' transform in DataBrew to change the column data type to date.
Why it's wrong here
The 'Format' transform in DataBrew changes the display format of a column but does not parse and convert multiple inconsistent source formats into a single standard. It assumes the data is already in a recognized date type or a single consistent format. For mixed formats like '2023-01-15', '01/15/2023', and '15-Jan-2023', the 'Standardize date' transform is required to interpret and convert each variant.
- ✗
Use the 'Replace value' transform to manually replace each non-ISO date string with its ISO equivalent.
Why it's wrong here
The 'Replace value' transform performs literal or pattern-based string replacement and does not understand date semantics. Manually mapping every possible non-ISO date variant to ISO would be error-prone, incomplete, and unmaintainable as new formats appear. This approach fails to handle the dynamic nature of inconsistent date formats and does not leverage DataBrew's date-aware parsing capabilities.
- ✗
Write a custom Python function in a DataBrew recipe step using the 'Custom transform' option.
Why it's wrong here
While DataBrew does support custom transforms, using a custom Python function is unnecessary when a built-in transform already handles multiple date formats. The built-in 'Standardize date' transform is purpose-built for this scenario and avoids additional code maintenance. Custom transforms are better suited for logic that cannot be expressed with built-in transforms, but here the built-in option is the most efficient and least error-prone choice.
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
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,321-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.