MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column with date strings in the format 'YYYY-MM-DD'. The data scientist wants to extract the year, month, and day as separate features. Which Data Wrangler transform should be used?
⚠ Common exam trap
Many candidates confuse 'Parse date' with 'Encode categorical' because dates can be treated as categorical features, but the question specifically asks for extracting year, month, and day as separate features, which requires parsing the date string into its components, not encoding the entire date as a category.
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
✓
Parse date transform.
The 'Parse date' transform in Amazon SageMaker Data Wrangler is specifically designed to convert date strings into structured datetime components. By applying this transform to the 'YYYY-MM-DD' column, the data scientist can automatically extract year, month, and day as separate features, enabling downstream feature engineering without manual string parsing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Encode categorical transform.
Why it's wrong here
Encode categorical is for categorical encoding, not date parsing.
- ✗
Scale values transform.
Why it's wrong here
Scale values is for numerical scaling, not date parsing.
- ✓
Parse date transform.
Why this is correct
Parse date allows extracting date components from date strings.
- ✗
Handle missing transform.
Why it's wrong here
Handle missing is for missing values, not date parsing.
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