ADC Data Quality & Validation 3

Question 1

What is the main goal of data deduplication in a data quality process?

Accepted Answer

To identify and remove or merge duplicate records that represent the same real-world entity

Answer

Data deduplication finds and resolves records that refer to the same entity but appear multiple times, improving accuracy and reducing storage waste.

Question 2

Which data quality dimension measures how well data values align with the defined data types, formats, and domains specified in the data dictionary?

Accepted Answer

Validity

Answer

Validity measures whether data values conform to the rules and constraints defined for that attribute, including data type, format, and allowable domain values.

Question 3

In a data validation pipeline, what is the purpose of a 'reject table'?

Accepted Answer

To capture records that fail validation rules so they can be reviewed and corrected

Answer

A reject table isolates records that did not pass one or more validation checks, allowing data stewards to investigate and remediate the errors before reprocessing.

Question 4

Which technique is used to standardize inconsistently formatted data values, such as converting 'United States,' 'US,' and 'U.S.A.' to a single canonical form?

Accepted Answer

Data parsing and standardization

Answer

Data parsing and standardization breaks down free-form values and maps them to a consistent, canonical format based on defined rules or reference tables.

Question 5

A data quality scorecard is primarily used to:

Accepted Answer

Track and communicate data quality metrics across multiple dimensions over time

Answer

A data quality scorecard provides a structured, measurable summary of quality dimensions such as accuracy, completeness, and consistency, enabling ongoing monitoring and improvement.

ADC - Access Data Certification Practice Test

ADC - Access Data Certification Practice Test

ADC Data Quality & Validation 3