ADC Data Quality & Validation 1 — Questions and Answers
Question 1: Which data quality dimension refers to the degree to which data correctly represents the real-world construct it is intended to model?
- Completeness
- Accuracy (Correct answer)
- Timeliness
- Consistency
Correct answer: Accuracy
Accuracy is the dimension that measures how closely data values reflect the true, real-world values they are intended to represent.
Question 2: A dataset is considered 'complete' when it:
- Contains no duplicate records
- Has all required fields populated with valid values (Correct answer)
- Is updated in real time
- Passes all referential integrity checks
Correct answer: Has all required fields populated with valid values
Completeness means all mandatory attributes are present and contain meaningful, non-null values rather than being empty or missing.
Question 3: Which validation technique checks that a data value falls within a predefined acceptable range?
- Format check
- Referential integrity check
- Range check (Correct answer)
- Cross-field validation
Correct answer: Range check
A range check validates that a value lies between a minimum and maximum threshold, such as ensuring a percentage field is between 0 and 100.
Question 4: What is the primary purpose of a checksum in data validation?
- To encrypt sensitive fields
- To detect errors introduced during data transmission or storage (Correct answer)
- To normalize numeric values
- To enforce foreign key constraints
Correct answer: To detect errors introduced during data transmission or storage
A checksum is a calculated value used to verify data integrity by detecting accidental changes or corruption during transmission or storage.
Question 5: Which of the following best describes 'data consistency'?
- Data is stored only once in the system
- The same data element holds the same value across all related datasets and systems (Correct answer)
- Data records are free from duplicates
- All fields in a record are populated
Correct answer: The same data element holds the same value across all related datasets and systems
Consistency requires that the same data item has the same value wherever it appears across different tables, databases, or systems.
Question 6: A 'uniqueness' constraint in data quality is violated when:
- A required field is left blank
- Two records share a value that should be exclusive to one record (Correct answer)
- A date field contains a future date
- A numeric field contains alphabetical characters
Correct answer: Two records share a value that should be exclusive to one record
Uniqueness violations occur when a value that must be unique—such as a social security number or primary key—appears in more than one record.
Question 7: Which data profiling technique is used to understand the distribution and frequency of values in a data column?
- Schema mapping
- Value frequency analysis (Correct answer)
- ETL lineage tracing
- Surrogate key generation
Correct answer: Value frequency analysis
Value frequency analysis examines how often each distinct value appears in a column, revealing patterns, outliers, and potential data quality issues.
ADC Data Quality & Validation 1
Which data quality dimension refers to the degree to which data correctly represents the real-world construct it is intended to model?