CompTIA Updated DA0-001 Exam Questions and Answers by solomon

Handling missing datais crucial for maintaining the integrity of an analysis. Since the missing value belongs to anemployedindividual, the most appropriate method is toimpute the mean income of employed citizens.

Option A (Replace the missing value with the average of unemployed citizens):Incorrect. The missing income is for anemployedindividual, so it would be inappropriate to use the unemployed citizens' average.

Option B (Insert 0):Incorrect. Assigning 0 would be misleading since it does not reflect the income distribution for employed citizens.

Option C (Impute the mean of the other citizens' incomes):Correct.A common practice in data analytics ismean imputation, where missing values are replaced with the mean of similar cases (in this case, other employed citizens).

Option D (Exclude employed citizens from the analysis):Incorrect. This would remove valuable data and lead to biased results.

Although the specific table isn’t shown here, this question type in CompTIA-style practice typically presents a score table where some values:

Are outside the valid range (e.g., scores > 100 on a 0–100 test, or negative scores)

Or use non-numeric or nonsensical entries for a score field (e.g., “N/A”, “ABC” where numeric values are required)

When values violate defined business rules or constraints (e.g., “exam scores must be between 0 and 100 and numeric”), they are considered invalid data.

Why the other options are not the best reason:

Redundant data (B): Would mean repeated/duplicate copies of the same information. That doesn’t address bad or impossible values.

Data outliers (C): Outliers are extreme but possible values within the defined range. Here the driver for cleansing is not just extremity but rule violation (e.g., >100 on a test that maxes at 100).

Missing data (D): Would concern null/blank fields, which is a different quality issue.

The key phrase in this type of question is that some values do not conform to the allowed or logical rules of the field. In CompTIA Data+ terms, this is a classic case of Invalid data (A) and is a primary driver for data cleansing.

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Domain: Data Quality (identifying invalid, inconsistent, incomplete data).

CompTIA Data+ Official Study Guide – section on data quality dimensions, highlighting validity as adherence to defined business rules (e.g., allowed ranges for scores).