CDS Data Quality Management 3 — Questions and Answers
Question 1: Which of the following best describes 'data profiling' in a data quality context?
- Encrypting sensitive fields before storage
- Analyzing data to understand its structure, content, and quality characteristics (Correct answer)
- Defining business rules for acceptable data values
- Publishing data to a data marketplace
Correct answer: Analyzing data to understand its structure, content, and quality characteristics
Data profiling examines datasets to discover metadata, patterns, anomalies, and quality issues such as nulls, duplicates, and format inconsistencies.
Question 2: When implementing a 'right-first-time' data quality strategy, what is the primary focus?
- Retroactively cleansing historical data in bulk
- Preventing defects at the point of data entry or creation (Correct answer)
- Automating downstream error correction after ingestion
- Archiving low-quality records for future remediation
Correct answer: Preventing defects at the point of data entry or creation
Right-first-time focuses on defect prevention at the source rather than downstream correction, reducing the cost of poor data quality.
Question 3: A data quality rule flags all ZIP codes that do not match the U.S. five-digit or ZIP+4 format. This is an example of which type of rule?
- Referential integrity rule
- Format/pattern rule (Correct answer)
- Cross-field consistency rule
- Threshold rule
Correct answer: Format/pattern rule
Format (or pattern) rules validate that data values conform to a specific syntactic structure, such as a regular expression for ZIP codes.
Question 4: What is the COST OF POOR DATA QUALITY (CPDQ) model primarily used for?
- Estimating storage costs for duplicate records
- Quantifying the financial impact of data defects to justify DQ investments (Correct answer)
- Benchmarking data quality against industry peers
- Calculating the number of FTE hours spent on manual data entry
Correct answer: Quantifying the financial impact of data defects to justify DQ investments
The CPDQ model quantifies costs such as rework, failed decisions, compliance fines, and lost revenue to build a business case for data quality programs.
Question 5: Which data quality technique involves comparing data against a trusted external reference source to verify accuracy?
- Data normalization
- Data benchmarking
- Data certification
- Data reconciliation with a golden record (Correct answer)
Correct answer: Data reconciliation with a golden record
Reconciling data against a golden record or authoritative external source validates that internal data accurately reflects the real-world values it represents.
Question 6: In a data quality framework, what distinguishes a 'critical data element' (CDE) from a regular data element?
- CDEs are stored only in relational databases
- CDEs have a disproportionate impact on business outcomes if they are incorrect (Correct answer)
- CDEs are always encrypted at rest
- CDEs are never modified after initial entry
Correct answer: CDEs have a disproportionate impact on business outcomes if they are incorrect
Critical Data Elements are those whose accuracy, completeness, or timeliness directly drives key business decisions, regulatory reporting, or customer outcomes.
Question 7: Which of the following is a key output of a Data Quality Assessment (DQA)?
- A fully cleansed production dataset ready for use
- A prioritized inventory of data quality issues with business impact ratings (Correct answer)
- A new data model that eliminates all quality problems
- An automated ETL pipeline configured to reject bad data
Correct answer: A prioritized inventory of data quality issues with business impact ratings
A DQA produces a documented inventory of issues ranked by severity and business impact, which guides remediation priorities and resource allocation.
CDS Data Quality Management 3
Which of the following best describes 'data profiling' in a data quality context?