The answer is deleting rows with missing values, along with imputing a placeholder like 'Unknown' for categorical data and using mean or median imputation for numerical fields. These are valid methods because they each address missing data without introducing statistical bias or distorting the underlying distribution—deletion works when missingness is random and minimal, while placeholder imputation preserves dataset structure for nominal categories, and mean/median imputation maintains central tendency for continuous variables. On the CompTIA Data+ DA0-001 exam, this question tests your ability to distinguish between valid handling techniques and invalid ones like simply ignoring missing values or replacing them with zero without justification. A common trap is assuming deletion is always best, but the exam emphasizes that context matters—deletion can cause data loss, while imputation must match the data type. Remember the mnemonic "DIM" for Delete, Impute (placeholder), and Mean/Median to recall the three valid approaches when handling missing data.
DA0-001 Comparing and Contrasting Data Concepts Practice Question
This DA0-001 practice question tests your understanding of comparing and contrasting data concepts. 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 THREE of the following are valid methods for handling missing data?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Using a placeholder like 'Unknown' for categorical data
Option A is correct because using a placeholder like 'Unknown' for categorical missing data preserves the dataset's structure and allows analysis to proceed without introducing statistical bias. This method is particularly valid for nominal data where the missing category can be treated as a distinct value, enabling downstream operations like one-hot encoding or frequency analysis without distorting the original distribution.
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.
✓
Using a placeholder like 'Unknown' for categorical data
Why this is correct
Placeholder is a valid approach.
Related concept
Read the scenario before looking for a memorised answer.
✗
Ignoring missing values and proceeding with analysis
Why it's wrong here
Ignoring can lead to errors.
✓
Replacing missing values with the mean of the column
Why this is correct
Mean imputation is a standard technique.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sorting the data to bring missing values to the top
Why it's wrong here
Sorting does not handle missingness.
✓
Deleting rows with missing values
Why this is correct
Listwise deletion is a common method.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'handling missing data' with 'preprocessing steps'—sorting (Option D) is a data organization technique, not a valid method for dealing with missing values, and ignoring missing data (Option B) is often mistakenly considered acceptable in quick analyses, but it violates best practices for robust data science workflows.
Detailed technical explanation
How to think about this question
Under the hood, handling missing data often involves imputation techniques that rely on the data type and missingness mechanism (MCAR, MAR, MNAR). For categorical data, using a placeholder like 'Unknown' is a form of mode imputation but avoids distorting the mode if missingness is non-random; for numerical data, mean imputation reduces variance and can introduce bias if data is not MCAR. In real-world scenarios, such as healthcare datasets, improperly handling missing values (e.g., ignoring them) can lead to flawed predictive models that fail regulatory validation.
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.
Comparing and Contrasting Data Concepts — This question tests Comparing and Contrasting Data Concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Using a placeholder like 'Unknown' for categorical data — Option A is correct because using a placeholder like 'Unknown' for categorical missing data preserves the dataset's structure and allows analysis to proceed without introducing statistical bias. This method is particularly valid for nominal data where the missing category can be treated as a distinct value, enabling downstream operations like one-hot encoding or frequency analysis without distorting the original distribution.
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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