ADC Data Quality & Validation

Question 1

Which data quality dimension refers to the degree to which data correctly represents the real-world construct it is intended to model?

Accepted Answer

Accuracy

Answer

Completeness

Answer

Timeliness

Answer

Consistency

Question 2

A dataset is considered 'complete' when it:

Accepted Answer

Has all required fields populated with valid values

Answer

Contains no duplicate records

Answer

Is updated in real time

Answer

Passes all referential integrity checks

Question 3

Which validation technique checks that a data value falls within a predefined acceptable range?

Accepted Answer

Range check

Answer

Format check

Answer

Referential integrity check

Answer

Cross-field validation

Question 4

What is the primary purpose of a checksum in data validation?

Accepted Answer

To detect errors introduced during data transmission or storage

Answer

To encrypt sensitive fields

Answer

To normalize numeric values

Answer

To enforce foreign key constraints

Question 5

Which of the following best describes 'data consistency'?

Accepted Answer

The same data element holds the same value across all related datasets and systems

Answer

Data is stored only once in the system

Answer

Data records are free from duplicates

Answer

All fields in a record are populated

Question 6

A 'uniqueness' constraint in data quality is violated when:

Accepted Answer

Two records share a value that should be exclusive to one record

Answer

A required field is left blank

Answer

A date field contains a future date

Answer

A numeric field contains alphabetical characters

Question 7

Which data profiling technique is used to understand the distribution and frequency of values in a data column?

Accepted Answer

Value frequency analysis

Answer

Schema mapping

Answer

ETL lineage tracing

Answer

Surrogate key generation