- A
First-order differencing
Why wrong: First-order differencing removes trend or seasonality, but does not stabilize variance; it is used for making a time series stationary in mean, not variance.
- B
Box-Cox transformation
Why wrong: Box-Cox transformation can stabilize variance, but it requires all data to be positive and involves estimating a parameter; the log transformation is a simpler and common special case.
- C
Log transformation
Log transformation is specifically used when variance increases with the mean; it compresses the scale and stabilizes variance, making it the most appropriate choice.
- D
Min-max scaling
Why wrong: Min-max scaling rescales data to a range [0,1] but does not stabilize variance; it is a normalization technique, not a variance-stabilizing transformation.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 analyst is working with a time series dataset that shows increasing variance over time. To stabilize the variance before modeling, which transformation 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
Log transformation
The log transformation (option C) is appropriate when variance increases with the mean, which is common in time series data. It compresses the scale and stabilizes variance. First-order differencing (A) is used to remove trend or seasonality, not to stabilize variance. The Box-Cox transformation (B) can also stabilize variance, but it is a more general family that includes log as a special case; however, log is simpler and often preferred when the data are positive. Min-max scaling (D) rescales to a fixed range but does not address changing variance.
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.
- ✗
First-order differencing
Why it's wrong here
First-order differencing removes trend or seasonality, but does not stabilize variance; it is used for making a time series stationary in mean, not variance.
- ✗
Box-Cox transformation
Why it's wrong here
Box-Cox transformation can stabilize variance, but it requires all data to be positive and involves estimating a parameter; the log transformation is a simpler and common special case.
- ✓
Log transformation
Why this is correct
Log transformation is specifically used when variance increases with the mean; it compresses the scale and stabilizes variance, making it the most appropriate choice.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Min-max scaling
Why it's wrong here
Min-max scaling rescales data to a range [0,1] but does not stabilize variance; it is a normalization technique, not a variance-stabilizing transformation.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Log transformation — The log transformation (option C) is appropriate when variance increases with the mean, which is common in time series data. It compresses the scale and stabilizes variance. First-order differencing (A) is used to remove trend or seasonality, not to stabilize variance. The Box-Cox transformation (B) can also stabilize variance, but it is a more general family that includes log as a special case; however, log is simpler and often preferred when the data are positive. Min-max scaling (D) rescales to a fixed range but does not address changing variance.
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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Last reviewed: Jun 20, 2026
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