CDP Data Quality Management 1 — Questions and Answers
Question 1: Which data quality dimension measures whether data values conform to the correct format, type, and range?
- Completeness
- Accuracy
- Validity (Correct answer)
- Timeliness
Correct answer: Validity
Validity measures whether data values conform to defined formats, data types, and allowable ranges or domain values.
Question 2: What is data profiling in the context of data quality?
- Encrypting sensitive data before storage
- Analyzing data to understand its structure, content, and quality characteristics (Correct answer)
- Creating user access profiles for data systems
- Compressing data to reduce storage
Correct answer: Analyzing data to understand its structure, content, and quality characteristics
Data profiling examines source data to understand its structure, distributions, patterns, completeness, and anomalies as a foundation for data quality improvement.
Question 3: Which data quality dimension refers to data being available when needed for decision-making?
- Accuracy
- Completeness
- Consistency
- Timeliness (Correct answer)
Correct answer: Timeliness
Timeliness measures whether data is available and up-to-date when needed by business processes and decision-makers.
Question 4: What is a data quality scorecard used for?
- Ranking database performance metrics
- Tracking and communicating the status of data quality metrics against defined targets over time (Correct answer)
- Scoring candidates in data entry job interviews
- Evaluating ETL tool vendors
Correct answer: Tracking and communicating the status of data quality metrics against defined targets over time
A data quality scorecard tracks key quality metrics against targets and communicates trends over time to stakeholders and governance teams.
Question 5: Which approach to fixing data quality issues addresses the root cause in source systems rather than cleansing data downstream?
- Reactive Cleansing
- Data Masking
- Upstream Fix / Prevention at Source (Correct answer)
- Deduplication
Correct answer: Upstream Fix / Prevention at Source
Fixing quality issues at the source prevents them from propagating downstream, which is more effective and efficient than repeatedly cleansing after the fact.
Question 6: What is the 'fitness for purpose' concept in data quality?
- Data must pass all six quality dimensions to be usable
- Data quality is evaluated in terms of whether data adequately meets the specific needs of its intended use (Correct answer)
- Data fitness is measured by storage efficiency
- Data must be formatted in a universal standard to be fit for use
Correct answer: Data quality is evaluated in terms of whether data adequately meets the specific needs of its intended use
Fitness for purpose means data quality is contextual—data is considered 'good enough' if it meets the specific requirements of its intended business use case.
CDP Data Quality Management 1
Which data quality dimension measures whether data values conform to the correct format, type, and range?