Top 25 Data Architecture Interview Questions and Answers

Top 25 Data Architecture Interview Questions and Answers

Data infrastructure design and management form the foundational layer for sophisticated analytics and machine learning systems.

Q1. What is Data Architecture?

Data Architecture is the design framework that defines how data is collected, stored, managed, and used across an organization.

Q2. Why is Data Architecture important?

It ensures data consistency, quality, accessibility, and security, enabling better decision-making and business efficiency.

Q3. What are the key components of Data Architecture?

Key components include data models, data storage, integration pipelines, metadata, governance, and security frameworks.

Q4. What is a Data Model?

A data model is a conceptual representation of data structures and relationships, guiding how data is organized and accessed.

Q5. Difference between Logical and Physical Data Models?

Logical models define abstract relationships and rules, while physical models specify actual database structures and implementation details.

Q6. What is Master Data?

Master Data refers to core business entities such as customers, products, and employees that are shared across systems.

Q7. What is Metadata?

Metadata is data about data, describing attributes like source, format, usage, and lineage of datasets.

Q8. What is Data Governance?

Data Governance is the set of policies, roles, and processes that ensure proper management and accountability of data assets.

Q9. What is Data Quality?

Data Quality measures the accuracy, completeness, consistency, and reliability of data used in business processes.

Q10. What is a Data Warehouse?

A Data Warehouse is a centralized repository optimized for reporting and analytics, integrating data from multiple sources.

Q11. Difference between OLTP and OLAP?

OLTP systems handle real-time transactional data, while OLAP systems support analytical queries and reporting.

Q12. What is a Data Lake?

A Data Lake is a storage repository that holds raw, unstructured, and structured data at scale for flexible analysis.

Q13. What is ETL?

ETL stands for Extract, Transform, Load — the process of moving data from source systems into a target repository.

Q14. What is ELT?

ELT stands for Extract, Load, Transform — data is loaded first into the target system and then transformed within it.

Q15. What is Data Integration?

Data Integration combines data from different sources into a unified view, enabling consistency and accessibility.

Q16. What is Data Lineage?

Data Lineage tracks the origin, movement, and transformation of data across systems to ensure transparency and trust.

Q17. What is a Data Pipeline?

A Data Pipeline is a series of processes that automate the movement and transformation of data between systems.

Q18. What is Big Data?

Big Data refers to large, complex datasets that require advanced tools and techniques for storage, processing, and analysis.

Q19. What is Cloud Data Architecture?

Cloud Data Architecture leverages cloud platforms to store, process, and manage data with scalability and flexibility.

Q20. What is Data Security?

Data Security involves protecting data from unauthorized access, corruption, or theft through encryption and access controls.

Q21. What is a Data Catalog?

A Data Catalog is a centralized inventory of data assets that helps users discover, understand, and use data effectively.

Q22. What is Structured Data?

Structured Data is organized in predefined formats such as tables, making it easy to store and query.

Q23. What is Unstructured Data?

Unstructured Data includes formats like text, images, and videos that lack a predefined schema.

Q24. What is Semi-Structured Data?

Semi-Structured Data has some organizational properties, like JSON or XML, but is not strictly tabular.

Q25. What is Data Architecture’s role in AI?

Data Architecture provides the foundation for AI by ensuring high-quality, well-structured, and accessible training data.

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