Sample Questions
Question 1
Easy
Which of the following BEST describes metadata in the context of data governance?
Solution
A is correct.
Metadata is data about data. It includes information such as data definitions (what each field means), data types and formats, source system origins, transformation rules applied during ETL, data lineage (how data flows and changes through systems), and ownership information. Metadata is foundational to data governance because it enables users to find, understand, trust, and properly use the organization's data assets.
Metadata is data about data. It includes information such as data definitions (what each field means), data types and formats, source system origins, transformation rules applied during ETL, data lineage (how data flows and changes through systems), and ownership information. Metadata is foundational to data governance because it enables users to find, understand, trust, and properly use the organization's data assets.
Question 2
Medium
A municipal utility must select one finance process for an unattended RPA pilot. Based on standard RPA suitability criteria, which process is the most suitable candidate?
Solution
B is correct.
RPA is best suited to high-volume, repetitive, rule-based processes using structured digital inputs. Posting 9,200 daily meter-reading records from a fixed-layout vendor file against the customer master file fits these characteristics. The other processes require judgment about business justification, estimation assumptions, or qualitative explanations.
RPA is best suited to high-volume, repetitive, rule-based processes using structured digital inputs. Posting 9,200 daily meter-reading records from a fixed-layout vendor file against the customer master file fits these characteristics. The other processes require judgment about business justification, estimation assumptions, or qualitative explanations.
Question 3
Hard
Claims data from a mainframe, policy platform, and medical records API include unmatched policy numbers, incompatible dates, and truncated mainframe descriptions. To address all three issues, the ETL transformation steps should include:
Solution
A is correct.
The three data quality issues require distinct transformation steps within the ETL pipeline. Referential integrity validation checks each claim's policy number against the policy master table, flagging or rejecting claims that reference nonexistent policies. Date format standardization converts incompatible date representations into a uniform format compatible with the warehouse schema. Field-length validation detects truncated description fields and triggers correction requests to the source system team to increase the export field width or provide supplementary data. Missing source content cannot be reliably reconstructed during transformation alone.
The three data quality issues require distinct transformation steps within the ETL pipeline. Referential integrity validation checks each claim's policy number against the policy master table, flagging or rejecting claims that reference nonexistent policies. Date format standardization converts incompatible date representations into a uniform format compatible with the warehouse schema. Field-length validation detects truncated description fields and triggers correction requests to the source system team to increase the export field width or provide supplementary data. Missing source content cannot be reliably reconstructed during transformation alone.
