Intermediate Data Governance test (Intermediate)

Sample questions

5 sample questions from the Intermediate Data Governance test, with the answers. Samples are taken out of the live test, so you will not meet these when you sit it.

1. A company noticed that its data governance practices were not yielding the expected results. During an audit, it was found that the data catalog was not providing accurate and up-to-date information. What is the most likely cause of this issue?

   1. The data lineage was not being captured correctly, making it impossible to trace the flow of data.
   2. The data access policies were too restrictive, preventing authorized users from accessing necessary data.
   3. The data classification labels were improperly assigned, leading to incorrect access controls.
   4. The data catalog was not being automatically updated with new metadata from data sources, leading to outdated and incomplete information.

   Answer: D. The data catalog was not being automatically updated with new metadata from data sources, leading to outdated and incomplete information.

   A data catalog requires continuous maintenance and updates to remain accurate and useful. If the catalog is not automatically updated with new metadata from data sources, it can become outdated, leading to incorrect decisions and governance issues. Manual updates alone are insufficient, as they are prone to errors and delays.

2. Which of the following is a key performance indicator (KPI) in data governance that provides traceability of data flows and supports impact analysis, compliance tracking, and traceability of data transformations?

   1. Data access audit trails
   2. Data catalog maintenance
   3. Data privacy impact assessments
   4. Data lineage

   Answer: D. Data lineage

   Data governance KPIs (Key Performance Indicators) are metrics used to measure the effectiveness of data governance initiatives. Among the options, data lineage directly supports KPIs by providing visibility into data flows, enabling impact analysis, compliance tracking, and traceability of data transformations. Data catalog maintenance, data access audit trails, and data privacy impact assessments are important aspects of data governance but do not directly measure performance, complexity, or internal workings in the same way as data lineage.

3. A company is unable to trace the flow of personal data (PII) through its data pipeline during a GDPR audit. What is the most likely reason for this issue?

   1. Column-level lineage was not captured, making it impossible to trace the PII through transformations.
   2. Data catalog metadata was not populated correctly.
   3. The data classification labels were misapplied.
   4. Data access policies were not enforced.

   Answer: A. Column-level lineage was not captured, making it impossible to trace the PII through transformations.

   Column-level lineage is necessary to trace PII through transformations, as table-level lineage is not sufficient for GDPR erasure or impact analysis. In this scenario, the lack of column-level lineage would prevent identifying which specific columns contain PII and how they flow through the system, leading to compliance issues.

4. A company has noticed that the data catalogue for its data platform is not keeping up with changes in the data assets. What is the most likely reason for this issue?

   1. The data catalogue is not being automatically updated with metadata from the data platform, leading to outdated and incomplete information.
   2. The data catalogue is not being integrated with the data privacy compliance tools.
   3. The data catalogue is not aligned with the data lineage tracking system.
   4. The data catalogue is not being used by data stewards for defining business glossary terms.

   Answer: A. The data catalogue is not being automatically updated with metadata from the data platform, leading to outdated and incomplete information.

   The data catalogue must be maintained through automated harvesting via connectors/crawlers and human curation to ensure it stays up-to-date and accurate. If it's not automatically updated, it may contain outdated or incomplete information, which can lead to trust issues and incorrect decisions based on the data. Manual updates alone are not sufficient for a large and dynamic data platform.

5. Which approach is commonly used by data classification metadata governance tools to identify and label sensitive data at the column level?

   1. Row-level scanning with natural language processing
   2. File-level scanning with optical character recognition
   3. Column-level scanning with regex and machine learning models
   4. Database-level scanning with manual tagging

   Answer: C. Column-level scanning with regex and machine learning models

   Data classification metadata governance tools typically use column-level scanning with regex and machine learning models to identify and label sensitive data. This method allows for precise classification at the column level, which is crucial for compliance and data privacy. Other options may be used in certain cases but are not the standard approach for metadata governance tools.