Question 1: What is the difference between PaaS and SaaS in Azure data services?

Answer: PaaS (Synapse, Databricks): You manage code, Azure manages infrastructure. SaaS (Fabric, Power BI): Everything managed. PaaS offers more control; SaaS offers simplicity.

Question 2: How do you choose between Azure data services?

Answer: Synapse for SQL data warehousing, Databricks for Spark analytics, Fabric for unified SaaS, ADF for orchestration, Event Hubs for streaming.

Question 3: What is the benefit of using Azure for data engineering?

Answer: Managed services, global infrastructure, pay-as-you-go, integration with Microsoft ecosystem, and enterprise security/compliance.

Question 4: How do you implement a data platform on Azure?

Answer: ADLS Gen2 for storage, ADF for orchestration, Synapse/Databricks for compute, Purview for governance, Power BI for visualization.

Question 5: What is the difference between data engineer and data architect?

Answer: Data Engineer: Implements pipelines, transformations, and infrastructure. Data Architect: Designs overall data strategy, architecture, and standards.

Question 6: How do you handle real-time and batch in the same architecture?

Answer: Lambda architecture: Event Hubs for real-time, ADLS for batch, unified serving layer. Or use Kappa (stream-only) with Delta Lake.

Question 7: What is the benefit of using managed services?

Answer: Reduced operational overhead, automatic scaling, built-in security, and focus on business logic instead of infrastructure.

Question 8: How do you handle data privacy in analytics?

Answer: Data masking, anonymization, encryption, access controls, and compliance automation with Purview.

Question 9: What is the difference between data lake and data warehouse?

Answer: Data Lake: Raw data, schema-on-read, diverse formats. Data Warehouse: Structured data, schema-on-write, optimized for analytics.

Question 10: How do you implement dataOps?

Answer: CI/CD pipelines, automated testing, infrastructure as code, monitoring, and continuous improvement.

Question 11: What is the benefit of using Azure Data Factory?

Answer: Visual orchestration, 90+ connectors, managed runtimes, monitoring, and Git integration. Simplifies complex data pipeline management.

Question 12: How do you handle multi-cloud data engineering?

Answer: Use open standards (Parquet, Delta Lake), federated governance (Purview), and cloud-agnostic tools (Spark, Python).

Question 13: What is the difference between data mesh and data lake?

Answer: Data Mesh: Organizational model (domain ownership, data products). Data Lake: Technical architecture (centralized storage). Data Mesh can use Data Lake for implementation.

Question 14: How do you handle data governance at scale?

Answer: Automated discovery (Purview), classification, lineage tracking, business glossary, and federated governance model.

Question 15: What is the benefit of using Delta Lake?

Answer: ACID transactions, schema evolution, time travel, data skipping, and streaming support. Enables reliable data lake operations.

Question 16: How do you optimize cloud data costs?

Answer: Right-sizing, auto-pause, reserved capacity, lifecycle management, and cost monitoring with alerts.

Question 17: What is the difference between ETL and data integration?

Answer: ETL: Extract, Transform, Load (specific pattern). Data Integration: Broader concept including ETL, ELT, CDC, and real-time synchronization.

Question 18: How do you handle data quality at scale?

Answer: Automated validation (Great Expectations), monitoring, alerting, quarantine patterns, and data stewardship.

Question 19: What is the benefit of serverless analytics?

Answer: No infrastructure management, auto-scaling, pay-per-use. Use Synapse Serverless, Azure Functions, and Logic Apps.

Question 20: How do you implement data lineage?

Answer: Purview for automated lineage from Azure services, custom lineage via SDK, and documentation in business glossary.

Question 21: What is the difference between data catalog and data inventory?

Answer: Data Catalog: Metadata repository for discovery and governance. Data Inventory: List of data assets. Catalog provides richer metadata and governance.

Question 22: How do you handle data migration to cloud?

Answer: Assess source, choose migration strategy (lift-and-shift, modernize), validate data, and implement cutover plan.

Question 23: What is the benefit of using Spark on Azure?

Answer: Distributed processing, Delta Lake integration, notebook development, and integration with Azure data services.

Question 24: How do you implement security in data pipelines?

Answer: Managed Identities, encryption, RBAC, Private Endpoints, and audit logging.

Question 25: What is the future of data engineering?

Answer: Unified platforms (Fabric), AI-powered analytics, real-time everything, data mesh adoption, and serverless-first architectures.