The correct combination is to impute missing values with the median, apply robust scaling, and then log transform skewed variables. This works because the median is resistant to extreme outliers, unlike the mean, so it preserves central tendency even when data is corrupted. Robust scaling then uses the median and interquartile range to center and scale the data without being pulled by outliers, while the log transformation compresses the range of skewed predictors, stabilizing variance and improving linearity—both critical for regression assumptions. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of preprocessing steps for regression with outliers and missing values, a common scenario where candidates mistakenly choose mean imputation or standard scaling. A frequent trap is assuming all missing values should be dropped or that standard scaling works fine with outliers. Remember the mnemonic “MRL” for Median, Robust, Log—it directly counters the three data problems of missingness, outliers, and skew.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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.
A data scientist is analyzing a dataset with 100 variables and 5,000 records. The dataset has several missing values and a few extreme outliers. The goal is to build a regression model to predict a continuous target. Which combination of preprocessing steps is most likely to improve model performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Impute missing values with median, apply robust scaling, and then log transform skewed variables
Option A is correct because imputing missing values with the median is robust to outliers, robust scaling handles extreme values by using median and IQR, and log transformation reduces skewness in predictors. This combination preserves data integrity and stabilizes variance, which is critical for regression models on a dataset with 100 variables and 5,000 records.
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.
✓
Impute missing values with median, apply robust scaling, and then log transform skewed variables
Why this is correct
Median imputation is robust, robust scaling handles outliers, log transform handles skewness.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Impute missing values with mean, then use PCA for dimensionality reduction
Why it's wrong here
Mean imputation is sensitive to outliers, PCA may not be needed initially.
✗
Drop all rows with missing values, then apply min-max scaling
Why it's wrong here
Dropping rows reduces sample size and min-max scaling is sensitive to outliers.
✗
Remove outliers using Z-score, then apply standard scaling
Why it's wrong here
Z-score removes data points and standard scaling is still sensitive to remaining outliers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that mean imputation and standard scaling are universally safe, but the trap here is that outliers and skewness require robust methods like median imputation and robust scaling to avoid distorting the model.
Detailed technical explanation
How to think about this question
Robust scaling uses the median and interquartile range (IQR), making it resistant to extreme values, unlike standard scaling which uses mean and standard deviation. Log transformation is effective for right-skewed distributions, but it requires positive values; if zeros or negatives exist, a constant shift (e.g., log(x+1)) is needed. In real-world datasets with 100 variables, many may be skewed, and ignoring this can violate linear regression assumptions of normality and homoscedasticity.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Impute missing values with median, apply robust scaling, and then log transform skewed variables — Option A is correct because imputing missing values with the median is robust to outliers, robust scaling handles extreme values by using median and IQR, and log transformation reduces skewness in predictors. This combination preserves data integrity and stabilizes variance, which is critical for regression models on a dataset with 100 variables and 5,000 records.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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