DEA-C01 Data Operations and Support Practice Question
A company uses AWS Glue DataBrew to clean and transform data. A data engineer notices that a DataBrew recipe step that should remove duplicates is not working as expected. The dataset has millions of rows. What is the MOST likely reason?
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 dataset is not sorted by the columns used for deduplication
DataBrew's 'Remove duplicate rows' step identifies duplicates by comparing each row to the previous row in the dataset. If the data is not sorted by the key columns, duplicate rows may not be adjacent and thus will not be removed, causing the step to appear ineffective. Option A: The number of files does not directly affect deduplication logic. Option B: Null values may be treated as distinct, but the most likely issue is lack of sorting. Option D: DataBrew projects can use sampling for preview, but recipes are applied to the full dataset when run, so sampling does not prevent deduplication.
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 data source is an S3 bucket with a large number of files
Why it's wrong here
File count doesn't affect dedup logic.
- ✗
The dataset contains null values in the key columns
Why it's wrong here
Nulls are considered distinct values.
- ✓
The dataset is not sorted by the columns used for deduplication
Why this is correct
DataBrew's dedup is based on consecutive duplicates; sorting is required.
- ✗
The DataBrew project is using a sampling of the data
Why it's wrong here
Sampling might miss duplicates but step should still work on the sample.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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