Quick Answer
The answer is the Parse date transform. This is the correct choice because Amazon SageMaker Data Wrangler’s Parse date transform is purpose-built for converting date strings into structured datetime components, automatically extracting year, month, and day as separate features without requiring custom string parsing logic. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of Data Wrangler’s built-in transforms for common data preparation tasks, often appearing in scenarios where raw date columns need to be broken down for time-series or feature engineering workflows. A common trap is confusing this with the “Cast column type” transform, which only changes data types without splitting into components, or attempting manual regex parsing. Remember the memory tip: “Parse date parts, don’t just cast the type” — if you need separate year, month, and day columns, always reach for the Parse date transform.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Encode categorical transform.
Why wrong: Encode categorical is for categorical encoding, not date parsing.
Scale values transform.
Why wrong: Scale values is for numerical scaling, not date parsing.
Parse date transform.
Parse date allows extracting date components from date strings.
Handle missing transform.
Why wrong: Handle missing is for missing values, not date parsing.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Handle missing transform.
Why it's wrong here
Handle missing is for missing values, not date parsing.
Common exam traps
The trap here is that candidates may 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.
Detailed technical explanation
Under the hood, the 'Parse date' transform in Data Wrangler leverages Apache Spark's date parsing capabilities, allowing flexible format specification (e.g., 'yyyy-MM-dd') and automatic extraction of components like year, month, day, day of week, and quarter. A subtle behavior is that if the date string contains timezone information or inconsistent formats, the transform may fail or produce nulls unless a custom format is explicitly provided. In real-world scenarios, this is critical for time-series feature engineering, such as creating lag features or seasonal indicators from raw date columns.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-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 MLA-C01 exam.
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