Quick Answer
The answer is incompleteness. This is the correct choice because data quality incompleteness refers specifically to the absence of required values within a dataset, such as the missing state codes in customer address records. When fields that should contain data are left null or blank, the dataset becomes less reliable for analysis, as critical geographic information is missing. On the CompTIA Data+ DA0-001 exam, this concept tests your ability to distinguish incompleteness from other data quality dimensions like accuracy, consistency, or timeliness. A common trap is confusing missing data with inaccurate data—remember that incompleteness is about what is absent, not what is wrong. A useful memory tip: think of incompleteness as “holes in the data,” where the required pieces are simply not there.
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.
A data analyst is working with a dataset that contains customer names and addresses. Some records have missing state codes. Which data quality issue is this?
Duplication
Why wrong: Duplication is about repeated records, not missing data.
Incompleteness
Missing state codes make the record incomplete.
Outliers
Why wrong: Outliers are extreme values, not missing data.
Inconsistency
Why wrong: Inconsistency refers to contradictory data, not missing values.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Incompleteness
Incompleteness is the correct answer because missing state codes in customer address records represent a lack of required data. This is a classic example of incomplete data, where fields that should contain values are left null or blank, reducing the dataset's usability for analysis.
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.
Duplication
Why it's wrong here
Duplication is about repeated records, not missing data.
Incompleteness
Why this is correct
Missing state codes make the record incomplete.
Related concept
Read the scenario before looking for a memorised answer.
Outliers
Why it's wrong here
Outliers are extreme values, not missing data.
Inconsistency
Why it's wrong here
Inconsistency refers to contradictory data, not missing values.
Common exam traps
The trap here is that candidates may confuse incompleteness with inconsistency, but incompleteness is about missing data (nulls), while inconsistency is about contradictory data across records.
Detailed technical explanation
In data quality frameworks, incompleteness is often measured as the percentage of missing values per column. For example, in a dataset with 1000 customer records, if 50 have missing state codes, the incompleteness rate is 5%. This can be detected using SQL's IS NULL or COUNT(*) with CASE statements, and handling strategies include imputation (e.g., using ZIP code to infer state) or flagging for manual review.
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: Incompleteness — Incompleteness is the correct answer because missing state codes in customer address records represent a lack of required data. This is a classic example of incomplete data, where fields that should contain values are left null or blank, reducing the dataset's usability for analysis.
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 24, 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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