MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset that includes a 'timestamp' column. They want to create features that capture seasonality. Which feature engineering approach is most appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Extract hour of day and apply sine/cosine transformation.
Extracting hour of day and applying sine and cosine transformations captures the cyclic nature of time (e.g., midnight wrapping around to the next day). Option A (binning into fixed intervals) loses granularity and does not preserve cyclicity. Option B (converting to Unix epoch seconds) loses the cyclic pattern. Option D (one-hot encoding) creates many sparse features and does not capture order or cycles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bin timestamps into fixed intervals.
Why it's wrong here
Binning loses information about periodicity.
- ✗
Convert timestamp to Unix epoch seconds.
Why it's wrong here
Linear representation does not capture cycles.
- ✓
Extract hour of day and apply sine/cosine transformation.
Why this is correct
Sine/cosine encoding preserves cyclic nature.
- ✗
One-hot encode the timestamp column.
Why it's wrong here
One-hot encoding is for categorical variables, not timestamps.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.