The answer is AWS Glue ETL with built-in timestamp transforms, as it directly enables date feature extraction like day of the week, hour, and month without requiring custom code. AWS Glue provides pre-built transforms within its ETL jobs that automatically parse timestamp columns and decompose them into individual date and time components, making it the only service listed that offers a no-code solution for this specific feature engineering task. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of which AWS services are purpose-built for different stages of the ML pipeline—specifically, Glue for serverless data preparation versus Athena for SQL-based querying, SageMaker Data Wrangler for visual feature engineering within Studio, and QuickSight for visualization only. A common trap is assuming Athena’s SQL date functions qualify as “no-code,” but the question explicitly requires no custom code, which eliminates Athena. Memory tip: think “Glue sticks dates together” for ETL, but here it pulls them apart—Glue’s built-in transforms handle the timestamp splitting for you.
MLS-C01 Exploratory Data Analysis Practice Question
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 machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset includes a column 'transaction_date' with timestamps. The engineer wants to derive features such as day of the week, hour, and month for modeling. Which AWS service can be used directly to extract these features without writing custom code?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Glue ETL with built-in timestamp transforms
Option B is correct because AWS Glue provides built-in transforms in its ETL jobs to parse timestamps and extract date/time components. Option A is wrong because Athena is a query engine and can extract date parts using SQL, but that requires writing SQL queries, not a no-code solution. Option C is wrong because SageMaker Data Wrangler is a visual tool that can create features, but it requires a SageMaker Studio environment. Option D is wrong because QuickSight is a visualization tool, not for feature engineering.
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.
✓
AWS Glue ETL with built-in timestamp transforms
Why this is correct
AWS Glue provides transforms like 'ExtractTimestamp' to derive date components without custom code.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Athena with SQL date functions
Why it's wrong here
Athena requires writing SQL queries, which is not 'without writing custom code'.
✗
Amazon QuickSight
Why it's wrong here
QuickSight is for visualization and dashboards, not for feature extraction.
✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Data Wrangler is a visual tool but still requires user interaction to configure transformations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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 — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Glue ETL with built-in timestamp transforms — Option B is correct because AWS Glue provides built-in transforms in its ETL jobs to parse timestamps and extract date/time components. Option A is wrong because Athena is a query engine and can extract date parts using SQL, but that requires writing SQL queries, not a no-code solution. Option C is wrong because SageMaker Data Wrangler is a visual tool that can create features, but it requires a SageMaker Studio environment. Option D is wrong because QuickSight is a visualization tool, not for feature engineering.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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