Data Quality & Validation Flashcards
7 cards from real ADC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Data Quality & Validation flashcards as text
What is the main goal of data deduplication in a data quality process?
Answer: To identify and remove or merge duplicate records that represent the same real-world entity
Data deduplication finds and resolves records that refer to the same entity but appear multiple times, improving accuracy and reducing storage waste.
Which data quality dimension measures how well data values align with the defined data types, formats, and domains specified in the data dictionary?
Answer: Validity
Validity measures whether data values conform to the rules and constraints defined for that attribute, including data type, format, and allowable domain values.
In a data validation pipeline, what is the purpose of a 'reject table'?
Answer: To capture records that fail validation rules so they can be reviewed and corrected
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.
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?
Answer: Data parsing and standardization
Data parsing and standardization breaks down free-form values and maps them to a consistent, canonical format based on defined rules or reference tables.
A data quality scorecard is primarily used to:
Answer: Track and communicate data quality metrics across multiple dimensions over time
A data quality scorecard provides a structured, measurable summary of quality dimensions such as accuracy, completeness, and consistency, enabling ongoing monitoring and improvement.
What is 'referential integrity' in the context of relational data validation?
Answer: A constraint ensuring that a foreign key value in one table always corresponds to a valid primary key in the referenced table
Referential integrity enforces that every foreign key value must match an existing primary key in the parent table, preventing orphaned records.
Which of the following best describes a 'data quality rule' in an ADC context?
Answer: A defined condition or constraint that data must satisfy to be considered fit for use
A data quality rule is a formal, testable condition—such as 'customer age must be between 0 and 120'—that determines whether data meets the standards required for its intended use.