The answer is binning continuous variables into discrete intervals, along with extracting date components and creating interaction features. Binning transforms numerical data like age or income into categorical buckets, which helps models capture non-linear relationships and reduces noise from outliers. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how feature engineering techniques prepare raw data for algorithms, often appearing in scenario-based questions about exploratory data analysis. A common trap is confusing binning with normalization—remember that binning creates categories, not scaled values. Extracting date components like day of week or hour from timestamps reveals temporal patterns such as seasonality, while interaction features multiply two variables to capture combined effects, like price times quantity. For the exam, a useful memory tip is “BED”—Binning, Extraction of dates, and interaction features—to recall the three core techniques for enriching datasets before modeling.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Which THREE techniques are commonly used for feature engineering in exploratory data analysis? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Extracting date/time components like day of week or hour.
Option A is correct because extracting date/time components such as day of week, hour, or month from a timestamp is a standard feature engineering technique. It transforms a single datetime column into multiple categorical or cyclical features that can reveal temporal patterns like weekly seasonality or peak hours, which are often critical for time-series models.
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.
✓
Extracting date/time components like day of week or hour.
Why this is correct
Temporal features often reveal patterns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Using principal component analysis (PCA) to create new features.
Why it's wrong here
PCA reduces dimensionality, but is not typically considered feature engineering; it transforms features.
✗
Applying one-hot encoding to numerical features.
Why it's wrong here
One-hot encoding is for categorical features.
✓
Creating interaction features between variables.
Why this is correct
Interaction features capture combined effects.
Related concept
Read the scenario before looking for a memorised answer.
✓
Binning continuous variables into discrete intervals.
Why this is correct
Binning can capture non-linear relationships.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between feature engineering (creating new features from existing data) and dimensionality reduction (PCA) or encoding (one-hot encoding), leading candidates to mistakenly select PCA as a feature engineering technique when it is actually a preprocessing step for reducing feature space.
Detailed technical explanation
How to think about this question
Feature engineering in EDA focuses on creating interpretable, domain-relevant features that capture underlying data structure. For example, binning continuous variables (Option E) uses techniques like equal-width or equal-frequency binning to convert a continuous distribution into discrete intervals, which can help handle outliers or non-linear relationships. Interaction features (Option D) multiply or combine two or more variables to capture synergy effects, such as price × quantity in retail demand modeling.
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.
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: Extracting date/time components like day of week or hour. — Option A is correct because extracting date/time components such as day of week, hour, or month from a timestamp is a standard feature engineering technique. It transforms a single datetime column into multiple categorical or cyclical features that can reveal temporal patterns like weekly seasonality or peak hours, which are often critical for time-series models.
What should I do if I get this MLS-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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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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