The answer is removing rows with missing values, imputing with the mean or median, and imputing with the mode. These three techniques for handling missing data are appropriate because they address different data types and missingness patterns: deletion works when the missing proportion is low and random, while mean or median imputation preserves central tendency for numeric columns, and mode imputation is best for categorical variables. On the CompTIA Data+ DA0-001 exam, this question tests your ability to distinguish between valid cleaning methods and common traps like simply ignoring missing values or using mean imputation on heavily skewed data without considering the distribution. A frequent trick is that while mean imputation is standard for normally distributed data, median is preferred for skewed distributions to avoid bias. Remember the memory tip: “Delete for low, mean for bell, median for skew, mode for text” to quickly recall which technique fits each scenario.
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 cleaning a dataset and notices missing values in several columns. Which THREE techniques are appropriate for handling missing data? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Replace missing values with the mean or median
Option A is correct because replacing missing values with the mean (for normally distributed data) or median (for skewed data) is a standard imputation technique that preserves the central tendency of the dataset without introducing bias. This method is appropriate when the missingness is random and the proportion of missing data is low, as it maintains the sample size for analysis.
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.
✓
Replace missing values with the mean or median
Why this is correct
Imputation with mean/median is a common technique for numeric data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Ignore missing values and proceed with analysis
Why it's wrong here
Ignoring missing values can lead to biased results and incorrect conclusions.
✓
Predict missing values using regression
Why this is correct
Regression imputation uses other variables to estimate missing values and can be more accurate than simple imputation.
Related concept
Read the scenario before looking for a memorised answer.
✓
Remove rows with missing values
Why this is correct
Removing rows is acceptable if the missing data is random and the sample size remains adequate.
Related concept
Read the scenario before looking for a memorised answer.
✗
Always replace missing values with zero
Why it's wrong here
Replacing with zero is arbitrary and can distort the data distribution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that ignoring missing values (Option B) is acceptable, but the DA0-001 exam expects candidates to recognize that most analytical tools require explicit handling of nulls, and simply proceeding without action leads to runtime errors or flawed results.
Detailed technical explanation
How to think about this question
Imputation techniques like mean/median replacement assume data are missing completely at random (MCAR); if the missingness is related to unobserved factors (MNAR), these methods can introduce bias. Regression imputation (Option C) models the missing variable as a function of other predictors, preserving relationships but potentially underestimating variance. Listwise deletion (Option D) is valid only when missing data are MCAR and the sample size remains adequate, as it discards entire rows and can reduce statistical power.
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 practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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: Replace missing values with the mean or median — Option A is correct because replacing missing values with the mean (for normally distributed data) or median (for skewed data) is a standard imputation technique that preserves the central tendency of the dataset without introducing bias. This method is appropriate when the missingness is random and the proportion of missing data is low, as it maintains the sample size for analysis.
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.
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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