The answer is consistency, as the exhibit shows the same customer ID mapped to two different names, which directly violates the data quality consistency dimension. Consistency requires that data values be free from contradiction and adhere to uniform representation rules across a dataset; here, the conflicting entries for identifier C001 break referential integrity and create a logical inconsistency. On the CompTIA Data+ DA0-001 exam, this dimension is frequently tested through scenarios involving duplicate records, mismatched formats, or contradictory values within or across tables—often disguised as a uniqueness or accuracy trap. A common memory tip is to think of consistency as the “same story” rule: if two records claim to represent the same entity, their attributes must agree, or the data is inconsistent. Remember the mnemonic “C for Contradiction” to flag when identical identifiers yield conflicting details.
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.
Exhibit
2024-01-15 10:23:45 ERROR: DataTypeMismatchException - Column 'age' contains mixed data types: INT and VARCHAR. Pipeline 'user_profile_etl' failed.
Refer to the exhibit. Which data quality dimension is being violated?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Consistency
The exhibit shows the same customer ID (C001) associated with two different customer names ('John Smith' and 'Jon Smith'), which violates the consistency dimension. Consistency requires that data values be free from contradiction and adhere to the same representation rules across the dataset. Here, the conflicting names for the same identifier break referential integrity and data uniformity.
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.
✗
Uniqueness
Why it's wrong here
Uniqueness is about duplicate records, not data type issues.
✓
Consistency
Why this is correct
Consistency ensures data formats and values are uniform; mixed data types violate this.
Related concept
Read the scenario before looking for a memorised answer.
✗
Timeliness
Why it's wrong here
Timeliness refers to whether data is up-to-date, not data type issues.
✗
Completeness
Why it's wrong here
Completeness deals with missing values, not data type mixing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse consistency with uniqueness, assuming any conflict between rows must be a duplicate record issue, when in fact consistency violations involve contradictory values for the same identifier across multiple records.
Detailed technical explanation
How to think about this question
In relational databases, consistency is enforced through constraints like CHECK, UNIQUE, and FOREIGN KEY, as well as application-level validation rules. A real-world scenario is a CRM system where a customer's name is updated in one table but not propagated to related tables, causing the same ID to have different names in different rows—this breaks data integrity and can lead to incorrect reporting or billing errors.
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: Consistency — The exhibit shows the same customer ID (C001) associated with two different customer names ('John Smith' and 'Jon Smith'), which violates the consistency dimension. Consistency requires that data values be free from contradiction and adhere to the same representation rules across the dataset. Here, the conflicting names for the same identifier break referential integrity and data uniformity.
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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