Quick Answer
The correct answer is AWS Glue because it offers built-in transformations specifically designed to parse timestamps and extract date features like day of week and hour from string-based columns. AWS Glue’s dynamic frame and Spark-based ETL capabilities allow you to apply functions such as `to_date` or `from_utc_timestamp` directly within a visual or code-based job, making it the ideal service for this data preparation task. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of which service handles structured data transformation versus querying or orchestration—a common trap is confusing Amazon Athena’s SQL query ability with actual transformation logic. Remember that Glue transforms data, while Athena queries data already in place. A useful memory tip: “Glue sticks data together and reshapes it; Athena just asks questions.”
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 performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?
Amazon Athena
Why wrong: Athena is for querying data, not for transforming it.
Amazon SageMaker Studio
Why wrong: SageMaker Studio is an IDE, not a data transformation service.
AWS Glue
AWS Glue provides built-in transformations for timestamp parsing and feature extraction.
AWS Data Pipeline
Why wrong: Data Pipeline is for workflow orchestration, not timestamp 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
AWS Glue
Option C is correct because AWS Glue provides built-in transformations to parse timestamps and extract date/time features. Option A is wrong because Amazon Athena is a query service, not a transformation service. Option B is wrong because Amazon SageMaker Studio is an IDE, not a data transformation service. Option D is wrong because AWS Data Pipeline is a workflow orchestration service, not a timestamp parsing tool.
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 Athena
Why it's wrong here
Athena is for querying data, not for transforming it.
Amazon SageMaker Studio
Why it's wrong here
SageMaker Studio is an IDE, not a data transformation service.
AWS Glue
Why this is correct
AWS Glue provides built-in transformations for timestamp parsing and feature extraction.
Related concept
Read the scenario before looking for a memorised answer.
AWS Data Pipeline
Why it's wrong here
Data Pipeline is for workflow orchestration, not timestamp parsing.
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: AWS Glue — Option C is correct because AWS Glue provides built-in transformations to parse timestamps and extract date/time features. Option A is wrong because Amazon Athena is a query service, not a transformation service. Option B is wrong because Amazon SageMaker Studio is an IDE, not a data transformation service. Option D is wrong because AWS Data Pipeline is a workflow orchestration service, not a timestamp parsing tool.
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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