The answer is removing duplicate records, imputing missing values, and standardizing data formats are three common steps in data cleaning. Imputing missing values is a critical step because real-world datasets frequently contain gaps from collection errors or system failures, and techniques like mean or median imputation, regression imputation, or k-NN algorithms help preserve sample size and avoid bias from simply dropping rows. On the CompTIA Data+ DA0-001 exam, this concept tests your understanding of data quality and preparation, often appearing in scenario-based questions where you must identify which cleaning actions address specific issues like incomplete or inconsistent data. A common trap is confusing data cleaning with data transformation or aggregation, so remember that cleaning focuses on fixing errors and filling gaps, not reshaping the dataset. For a quick memory tip, think of the three C’s: Clean duplicates, Complete missing values, and Correct formats.
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 THREE of the following are common steps in data cleaning?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Imputing missing values
Imputing missing values is a common data cleaning step because real-world datasets often have gaps due to data collection errors or system failures. Techniques like mean/median imputation, regression imputation, or using algorithms like k-NN help preserve sample size and avoid bias that would result from simply dropping rows. This ensures the dataset remains usable for analysis without introducing significant distortion.
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.
✗
Removing outliers without justification
Why it's wrong here
Outliers should only be removed with a valid reason, not automatically.
✓
Imputing missing values
Why this is correct
Missing values are often imputed to maintain dataset completeness.
Related concept
Read the scenario before looking for a memorised answer.
✓
Standardizing data formats
Why this is correct
Standardizing formats ensures consistency across data (e.g., date formats).
Related concept
Read the scenario before looking for a memorised answer.
✓
Removing duplicate records
Why this is correct
Duplicates distort analysis and are removed during cleaning.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increasing sample size
Why it's wrong here
Increasing sample size is a data collection strategy, not a cleaning step.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between data cleaning steps and data collection or preprocessing steps, so the trap here is confusing 'increasing sample size' (a data augmentation or collection activity) with actual cleaning tasks like imputation, standardization, and deduplication.
Detailed technical explanation
How to think about this question
Data cleaning often involves handling missing data via imputation methods such as mean/median for numerical features or mode for categorical features, but more advanced techniques like multiple imputation (e.g., using MICE in Python's `fancyimpute`) account for uncertainty. Standardizing data formats (e.g., converting dates to ISO 8601 or normalizing text to lowercase) ensures consistency across sources, while deduplication relies on exact or fuzzy matching algorithms (e.g., Levenshtein distance) to identify and merge duplicate records. In real-world scenarios, failing to standardize formats can cause joins to fail silently, leading to incorrect analysis.
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: Imputing missing values — Imputing missing values is a common data cleaning step because real-world datasets often have gaps due to data collection errors or system failures. Techniques like mean/median imputation, regression imputation, or using algorithms like k-NN help preserve sample size and avoid bias that would result from simply dropping rows. This ensures the dataset remains usable for analysis without introducing significant distortion.
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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