- A
Consistency
Data is uniform across systems.
- B
Completeness
All required data is present.
- C
Accuracy
Data correctly reflects real-world values.
- D
Velocity
Why wrong: Velocity is a characteristic of big data, not quality.
- E
Volume
Why wrong: Volume is a quantity measure, not a quality dimension.
Question 616 of 1,000
DA0-001 Data Concepts and Environments Practice Question
This DA0-001 practice question tests your understanding of data concepts and environments. 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.
A data governance team is establishing policies for data quality. Which THREE of the following are common dimensions of data quality? (Select 3)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Consistency
Consistency is a common dimension of data quality because it ensures that data values are uniform across different datasets or systems, preventing contradictions. For example, if a customer's address is stored as '123 Main St' in one database and '123 Main Street' in another, consistency rules would flag this discrepancy. This dimension is critical for reliable reporting and integration.
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.
- ✓
Consistency
Why this is correct
Data is uniform across systems.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Completeness
Why this is correct
All required data is present.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Accuracy
Why this is correct
Data correctly reflects real-world values.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Velocity
Why it's wrong here
Velocity is a characteristic of big data, not quality.
- ✗
Volume
Why it's wrong here
Volume is a quantity measure, not a quality dimension.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the characteristics of big data (velocity, volume, variety) with the dimensions of data quality, leading them to select velocity or volume instead of the correct quality-focused options.
Detailed technical explanation
How to think about this question
Data quality dimensions like consistency, completeness, and accuracy are defined in frameworks such as ISO 8000, the international standard for data quality. Consistency often relies on referential integrity constraints in relational databases (e.g., foreign key relationships) or data profiling tools that detect format mismatches. In practice, a data governance team might use automated rules to enforce consistency across source systems, such as ensuring date formats follow ISO 8601 (YYYY-MM-DD) universally.
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.
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FAQ
Questions learners often ask
What does this DA0-001 question test?
Data Concepts and Environments — This question tests Data Concepts and Environments — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Consistency — Consistency is a common dimension of data quality because it ensures that data values are uniform across different datasets or systems, preventing contradictions. For example, if a customer's address is stored as '123 Main St' in one database and '123 Main Street' in another, consistency rules would flag this discrepancy. This dimension is critical for reliable reporting and integration.
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.
About these practice questions
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Last reviewed: Jul 4, 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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