The correct answer is to use the built-in 'day_of_week' transform on the 'review_date' column in SageMaker Data Wrangler. This transform directly extracts the day of the week—such as Monday or Tuesday—from a timestamp column without requiring any custom code or additional formatting, because Data Wrangler includes this as an optimized, native transformation within its visual interface. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your familiarity with Data Wrangler’s built-in feature engineering capabilities versus writing custom PySpark or pandas logic, which is a common trap for candidates who overcomplicate the solution. The key is to remember that Data Wrangler provides a dedicated 'day_of_week' operation under the "Add Transform" menu for timestamp columns, making it the fastest and most maintainable approach. Memory tip: think "DOW" for "Day Of Week" — Data Wrangler has it built-in, so don't reinvent the wheel.
MLA-C01 Data Preparation for Machine Learning Practice Question
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 engineer is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column 'review_date' with timestamps. The engineer wants to extract the day of the week as a new feature. How should this transformation be performed in Data Wrangler?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the 'day_of_week' transform on the 'review_date' column
Option D is correct because Amazon SageMaker Data Wrangler includes a built-in 'day_of_week' transform that directly extracts the day of the week (e.g., Monday, Tuesday) from a timestamp column without requiring custom code or additional formatting. This transform is optimized for Data Wrangler's visual interface and integrates seamlessly with its processing pipeline.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Write a custom Python script using pandas dt.day_name()
Why it's wrong here
Unnecessarily complex when built-in transform exists.
✗
Use one-hot encoding on the timestamp
Why it's wrong here
One-hot encoding is for categorical features, not extraction.
✗
Use the 'extract' transform with format '%A'
Why it's wrong here
Not a supported transform in Data Wrangler.
✓
Use the 'day_of_week' transform on the 'review_date' column
Why this is correct
Built-in transform extracts day of week (Monday=0, etc.).
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between built-in transforms and custom scripting, and the trap here is that candidates may assume they need to write a Python script (Option A) because they are familiar with pandas, overlooking Data Wrangler's native 'day_of_week' transform that is simpler and more appropriate for the visual workflow.
Detailed technical explanation
How to think about this question
Under the hood, Data Wrangler's 'day_of_week' transform leverages the underlying Spark or Pandas engine to parse the timestamp and map it to a day name (e.g., 0=Monday, 6=Sunday) based on the locale, handling timezone-aware columns correctly. A subtle behavior is that the transform respects the DataFrame's timezone setting, so if the 'review_date' column is timezone-naive, it assumes UTC; this can lead to off-by-one day errors if the data is actually in a different timezone. In a real-world scenario, an e-commerce company might use this to analyze sales patterns by day of the week, and failing to account for timezone could misattribute weekend sales to weekdays.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the 'day_of_week' transform on the 'review_date' column — Option D is correct because Amazon SageMaker Data Wrangler includes a built-in 'day_of_week' transform that directly extracts the day of the week (e.g., Monday, Tuesday) from a timestamp column without requiring custom code or additional formatting. This transform is optimized for Data Wrangler's visual interface and integrates seamlessly with its processing pipeline.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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