Quick Answer
The answer is completeness. Completeness is the data quality dimension that is compromised when a missing value, such as Charlie’s salary, leaves a record without all required fields. In technical terms, completeness is measured by the proportion of non-null values within a dataset; a null entry directly fails this check, as the dimension demands that every necessary attribute be populated. On the CompTIA Data+ DA0-001 exam, you will often see scenarios where a missing field—like a salary, email, or customer ID—tests your ability to distinguish completeness from other dimensions like accuracy or consistency. A common trap is confusing a missing value with an inaccurate one, but remember: completeness is about presence, not correctness. To lock this in, use the memory tip “Null means not full”—if a field is null, completeness is nullified.
This DA0-001 practice question tests your understanding of comparing and contrasting data concepts. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
ID, Name, Salary, Department 1, Alice, 60000, Sales 2, Bob, 70000, IT 3, Charlie, , IT
Refer to the exhibit. Which data quality dimension is compromised by the missing value for Charlie's salary?
ID, Name, Salary, Department 1, Alice, 60000, Sales 2, Bob, 70000, IT 3, Charlie, , IT
Uniqueness
Why wrong: Incorrect. Uniqueness concerns duplicate records.
Completeness
Correct. The salary field is missing, so data is incomplete.
Timeliness
Why wrong: Incorrect. Timeliness relates to data being up-to-date.
Accuracy
Why wrong: Incorrect. Accuracy is about correctness; here data is missing, not incorrect.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Completeness
Completeness measures whether all required data is present. Charlie's missing salary value means the record is incomplete, directly violating this dimension. In data quality frameworks, completeness is assessed by the proportion of non-null values in a field, and a null salary here fails that check.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Uniqueness
Why it's wrong here
Incorrect. Uniqueness concerns duplicate records.
Completeness
Why this is correct
Correct. The salary field is missing, so data is incomplete.
Related concept
Read the scenario before looking for a memorised answer.
Timeliness
Why it's wrong here
Incorrect. Timeliness relates to data being up-to-date.
Accuracy
Why it's wrong here
Incorrect. Accuracy is about correctness; here data is missing, not incorrect.
Common exam traps
CompTIA often tests the distinction between 'missing' (completeness) and 'wrong' (accuracy), leading candidates to confuse a null value with an incorrect value.
Detailed technical explanation
In data quality management, completeness is often quantified as a percentage of non-null entries in a column. For example, in a database table, a NULL salary field indicates that the data was not captured, which can result from optional entry fields or failed ETL processes. Real-world scenarios like payroll systems require 100% completeness for salary fields to avoid compliance or reporting errors.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Comparing and Contrasting Data Concepts — This question tests Comparing and Contrasting Data Concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Completeness — Completeness measures whether all required data is present. Charlie's missing salary value means the record is incomplete, directly violating this dimension. In data quality frameworks, completeness is assessed by the proportion of non-null values in a field, and a null salary here fails that check.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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