The answer is mean imputation and regression imputation. Mean imputation replaces missing numerical values with the average of the observed values for that feature, preserving sample size while being simple to implement, though it can reduce variance and distort relationships if data is not missing completely at random. Regression imputation, on the other hand, uses the relationships between features to predict and fill missing values, offering a more sophisticated approach that maintains data integrity when correlations exist. On the CompTIA Data+ DA0-001 exam, handling missing data techniques are tested as part of data cleaning and preparation, often with a trap where candidates confuse deletion methods with imputation or assume all imputation is equally valid regardless of missingness patterns. A helpful memory tip: “Mean for quick, regression for relationships” — use mean imputation when speed and simplicity matter, but turn to regression imputation when you need to preserve the underlying structure of your data.
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.
Which TWO of the following are commonly used techniques for handling missing data in a dataset? (Select TWO).
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Mean imputation
Mean imputation is a commonly used technique for handling missing numerical data where the missing value is replaced with the mean of the observed values for that feature. It preserves the sample size and is simple to implement, though it can reduce variance and distort relationships if data is not missing completely at random.
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.
✓
Mean imputation
Why this is correct
Mean imputation replaces missing values with the mean of the column.
Related concept
Read the scenario before looking for a memorised answer.
✗
Mode imputation
Why it's wrong here
Mode imputation is for categorical data, not typically for numerical.
✗
Dropping columns with missing data
Why it's wrong here
Dropping columns is a technique but often loses data; it's not a common imputation technique.
✗
Dropping rows with missing data
Why it's wrong here
Dropping rows is a technique but not imputation; it's deletion.
✓
Regression imputation
Why this is correct
Regression imputation uses a regression model to predict missing values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between common imputation methods (mean, median, mode, regression) and data removal techniques, trapping candidates who confuse 'dropping rows' as a primary technique when imputation is more widely recommended for preserving data integrity.
Detailed technical explanation
How to think about this question
Mean imputation works by calculating the arithmetic mean of the non-missing values in a column and substituting it for each missing entry, which maintains the sample mean but artificially reduces variance and covariance estimates. Regression imputation uses a predictive model (e.g., linear regression) to estimate missing values based on other features, preserving relationships better than mean imputation but introducing model assumptions and potential overfitting. In practice, mean imputation is often a baseline method, while regression imputation is used when correlations among variables are strong.
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: Mean imputation — Mean imputation is a commonly used technique for handling missing numerical data where the missing value is replaced with the mean of the observed values for that feature. It preserves the sample size and is simple to implement, though it can reduce variance and distort relationships if data is not missing completely at random.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This DA0-001 practice question is part of Courseiva's free CompTIA 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 DA0-001 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.