Quick Answer
The answer is Amazon SageMaker Data Wrangler because it provides built-in, no-code transformations specifically designed for datetime feature engineering, such as extracting day of week, hour, and weekend flags directly from timestamp columns. This service is the correct choice for the AWS Certified Machine Learning Specialty MLS-C01 exam because it directly addresses the need for rapid feature creation without writing custom code, unlike AWS Glue ETL or Amazon EMR which require manual scripting. On the exam, this question tests your ability to distinguish between services that offer native feature engineering versus those that are general-purpose data processing tools. A common trap is selecting AWS Glue ETL, but remember that Glue’s built-in transforms do not include datetime extraction—you would need to write a Spark UDF. For a quick memory tip: think “Data Wrangler = Date Wrangler” for any timestamp feature extraction task.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 working with a dataset containing customer transactions. The dataset has a column named 'transaction_date' with timestamp values. The scientist wants to create new features such as day of week, hour, and whether the transaction occurred on a weekend. Which AWS service provides built-in feature engineering capabilities for datetime columns?
Amazon SageMaker Data Wrangler
Data Wrangler has built-in datetime feature extraction.
Amazon Athena
Why wrong: Athena can query but not directly engineer features.
AWS Glue ETL
Why wrong: Glue ETL requires custom Spark code for datetime features.
Amazon EMR
Why wrong: EMR is a platform for custom big data processing.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker Data Wrangler
Amazon SageMaker Data Wrangler includes built-in transformations for datetime features like extracting day, month, hour, etc. Option B (AWS Glue ETL) requires custom code. Option C (Amazon Athena) can extract parts but not as a feature engineering step. Option D (Amazon EMR) requires more manual effort.
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.
Amazon SageMaker Data Wrangler
Why this is correct
Data Wrangler has built-in datetime feature extraction.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Athena
Why it's wrong here
Athena can query but not directly engineer features.
AWS Glue ETL
Why it's wrong here
Glue ETL requires custom Spark code for datetime features.
Amazon EMR
Why it's wrong here
EMR is a platform for custom big data processing.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker Data Wrangler — Amazon SageMaker Data Wrangler includes built-in transformations for datetime features like extracting day, month, hour, etc. Option B (AWS Glue ETL) requires custom code. Option C (Amazon Athena) can extract parts but not as a feature engineering step. Option D (Amazon EMR) requires more manual effort.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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