Governance Intermediate Quiz 2
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Quiz 2
1. In Data Quality, what is 'Consistency' across systems?
Establishing retention routines so historical records are never purged from central servers.
Ensuring the same data element has the same value across different databases.
Organizing every database attribute under identical physical column layout constraint settings.
2. What is a 'Critical Data Element' (CDE)?
Data that has a high impact on regulatory reporting or business decisions.
Any unstructured analytics partition that grows larger than a defined storage threshold.
A database record field that has been protected using redundant multi-layer cipher keys.
3. Which DQ metric measures the interval between a real-world event and its availability in the system?
Validity, ensuring content strings match pattern format criteria rules accurately.
Uniqueness, confirming that individual entity profiles are not duplicated in tables.
Data Latency / Timeliness, tracking systemic delay gaps from collection to availability.
4. What is the purpose of a 'Negative Data Control'?
To track down and correct user experience design choices that frustrate business consumers.
To identify and flag data that should NOT be present (e.g., PII in a public field).
To run background routines that truncate old rows to recover storage drive capacity fractions.
5. What is 'Probabilistic Matching' in MDM?
Using statistical likelihood to identify that two records refer to the same entity.
Executing randomized algorithms to extract master reference records from a data pool.
Grouping transaction data sets matching identical character values exactly without variations.
6. What is a 'Data Quality Scorecard'?
A development exercise utilized to train engineering hires on database layout standards.
A document tracking annual competency evaluations across operational system engineering personnel.
A visual dashboard showing DQ health against predefined thresholds.
7. What is 'Survivorship' in the context of Master Data?
The structural resilience metric of cloud database architectures during network drop intervals.
The logic used to determine which attribute value wins when merging records.
The chronological storage duration calculated for oldest historical archives on system media.
8. What does 'Data Observability' add beyond traditional DQ monitoring?
Real-time monitoring of data pipeline health, volume shifts, and schema changes.
Visual exploration passes requiring manual checking of master record fields using interface viewports.
Contract oversight strategies onboarding external consulting teams to track company spreadsheet assets.
9. Which DQ dimension is violated if a 'Customer Age' field contains '-5'?
Uniqueness, because database tracking mechanisms require independent entry records.
Completeness, because data ingest pipelines must supply every required input column.
Validity / Reasonableness, because the field value breaks logical business domain boundaries.
10. What is 'Root Cause Analysis' (RCA) in governance?
Documenting historical engineering choices made during initial startup platform construction milestones.
Identifying the underlying reason why a data quality issue keeps recurring.
Analyzing physical cable placement matrices inside regional data hosting server warehouses.
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