Data Governance Consultant
Data Governance Consultant Interview Questions and Answers for Freshers
Q1. How do you handle slowly changing dimensions
Slowly changing dimensions are handled by using different types like Type 1, Type 2, or Type 3 depending on the requirements.
Use Type 1 for overwriting existing data with new values
Use Type 2 for creating new records for changes and maintaining history
Use Type 3 for creating separate columns for different versions of data
Implement effective date ranges to track changes over time
Q2. what is your experience with data governance
I have over 5 years of experience in implementing data governance frameworks, policies, and procedures.
Implemented data governance policies and procedures to ensure data quality and compliance
Developed data governance frameworks to establish roles, responsibilities, and processes
Conducted data quality assessments and implemented data quality improvement initiatives
Collaborated with cross-functional teams to align data governance practices with business objectives
Q3. what are root cause analysis strategies
Root cause analysis strategies are methods used to identify the underlying cause of a problem or issue.
Identifying the problem or issue that needs to be addressed
Collecting data and information related to the problem
Analyzing the data to determine possible causes
Using techniques such as the 5 Whys, Fishbone Diagram, or Pareto Analysis
Developing and implementing solutions to address the root cause
Q4. what is star schema and snowflake schema
Star schema is a data warehouse schema where a central fact table is connected to multiple dimension tables. Snowflake schema is a normalized form of star schema where dimension tables are further normalized into sub-dimension tables.
Star schema has a central fact table connected to multiple dimension tables
Snowflake schema is a normalized form of star schema with dimension tables further normalized into sub-dimension tables
Star schema is denormalized for faster query perform...read more
Q5. what is data remediation
Data remediation is the process of identifying, correcting, and preventing data quality issues within an organization's data assets.
Data remediation involves identifying incorrect, incomplete, or inconsistent data and taking steps to correct it.
It may include data cleansing, data enrichment, data standardization, and data deduplication.
Examples of data remediation tasks include removing duplicate records, updating outdated information, and ensuring data accuracy and consisten...read more
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