What are the typical steps to perform data quality testing across a Clarity model?

What are the typical steps to perform data quality testing across a Clarity model?

• A. Define quality rules, run automated validation tests, profile data, perform sampling reconciliations against source data, and track issues.
• B. Write code-only tests, skip profiling, random sampling, no issue tracking.
• C. Only rely on manual checks, ignore automated tests.
• D. Do not define rules, but assume data quality is fine.

Answer: (A)

Data quality testing for a Clarity model starts with setting clear quality rules for fields and relationships, so everyone has a shared standard for accuracy, completeness, validity, and consistency. Once those rules exist, you run automated validation tests that apply them across the model at scale, catching defects consistently every run rather than relying on manual checks. Profiling the data then provides a snapshot of current quality—null rates, value distributions, unique counts, and outliers—so you understand where problems are concentrated and where rules might need refinement. Performing sampling reconciliations against source data helps verify that data movement and transformations preserve fidelity; using samples keeps the check practical while still giving you confidence about the overall data. Finally, tracking issues creates a visible, auditable workflow for logging, prioritizing, and remediating defects, and for monitoring progress over time. Taken together, these steps form a repeatable, scalable approach to data quality in a Clarity model. Approaches that skip automated tests, skip profiling, or skip issue tracking—relying only on manual checks or assuming quality is fine—miss essential parts of the quality lifecycle and aren’t robust for ongoing data health.

Data quality testing for a Clarity model starts with setting clear quality rules for fields and relationships, so everyone has a shared standard for accuracy, completeness, validity, and consistency. Once those rules exist, you run automated validation tests that apply them across the model at scale, catching defects consistently every run rather than relying on manual checks. Profiling the data then provides a snapshot of current quality—null rates, value distributions, unique counts, and outliers—so you understand where problems are concentrated and where rules might need refinement. Performing sampling reconciliations against source data helps verify that data movement and transformations preserve fidelity; using samples keeps the check practical while still giving you confidence about the overall data. Finally, tracking issues creates a visible, auditable workflow for logging, prioritizing, and remediating defects, and for monitoring progress over time. Taken together, these steps form a repeatable, scalable approach to data quality in a Clarity model. Approaches that skip automated tests, skip profiling, or skip issue tracking—relying only on manual checks or assuming quality is fine—miss essential parts of the quality lifecycle and aren’t robust for ongoing data health.