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 recipe must replace all null values in a specific column with the string 'UNKNOWN' and then convert the column to uppercase. The engineer wants to apply these steps in a repeatable recipe. Which combination of DataBrew transforms should be used?
⚠ Common exam trap
The trap here is assuming that nulls can be handled with a regex-based replace transform, when DataBrew requires the dedicated fill-missing-values transform for null substitution.
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 'Fill missing values' transform with a custom value, then the 'Upper case' transform on the column.
DataBrew provides a dedicated 'Fill missing values' transform for substituting nulls with a constant, and an 'Upper case' transform for case normalization. Together they form a repeatable recipe that preserves rows and produces the required output. Removing nulls or using regex replacement does not achieve the same result.
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 'Fill missing values' transform with a custom value, then the 'Upper case' transform on the column.
Why this is correct
The 'Fill missing values' transform replaces nulls with a specified value such as 'UNKNOWN', and the 'Upper case' transform converts the column's text to uppercase. Applied in sequence within a recipe, they achieve the required cleaning and normalization. DataBrew recipes are repeatable and can be scheduled or run as jobs.
- ✗
Use the 'Remove nulls' transform, then the 'Upper case' transform.
Why it's wrong here
Removing nulls deletes entire rows where the column is null, which eliminates data rather than replacing it with 'UNKNOWN'. The scenario requires preserving the row and substituting a placeholder, so this approach loses records and does not meet the requirement. It also does not provide the specified replacement string.
- ✗
Use the 'Replace value' transform with a regex for null, then the 'Upper case' transform.
Why it's wrong here
The 'Replace value' transform matches existing values, but nulls are not matched by regex patterns in DataBrew. Nulls require the dedicated 'Fill missing values' transform. Using regex replacement on nulls would not reliably catch them, and the result would not consistently produce 'UNKNOWN'. This approach is unreliable for the stated goal.
- ✗
Use the 'Custom formula' transform with an IFNULL expression, then the 'Upper case' transform.
Why it's wrong here
DataBrew supports custom formulas via SQL-like expressions, but the 'Fill missing values' transform is the purpose-built, no-code option for null replacement. A custom formula could work but is more error-prone and less maintainable in a recipe. The scenario asks for the appropriate DataBrew transforms, and the built-in fill transform is the correct 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 |
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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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