Question 1: Design a real-time analytics platform on Azure.

Answer: Event Hubs (ingestion) → Stream Analytics (processing) → Cosmos DB (real-time storage) → Power BI (dashboards). Event Hubs Capture → ADLS Gen2 (batch analytics) → Synapse Serverless (exploration) → Synapse Dedicated (production).

Question 2: How do you design a data lake for multi-tenant scenarios?

Answer: Separate containers per tenant, or partition by tenant ID. Use ACLs for tenant isolation. Implement lifecycle management per tenant. Monitor costs per tenant with tags.

Question 3: What is the Lambda architecture?

Answer: Batch layer (historical processing), speed layer (real-time processing), serving layer (unified view). Use Synapse for batch, Stream Analytics for speed, Power BI for serving.

Question 4: How do you design for high availability?

Answer: Use Availability Zones, RA-GRS storage, multi-region Synapse, Cosmos DB multi-region writes, and automated failover.

Question 5: What is the Kappa architecture?

Answer: Stream-only architecture (no batch layer). All data processed as streams. Use Event Hubs + Stream Analytics for all analytics needs.

Question 6: How do you design a data mesh?

Answer: Domain ownership, data products, self-serve platform (ADLS, Synapse, Databricks), federated governance (Purview).

Question 7: What is the lakehouse pattern?

Answer: Combines data lake (ADLS Gen2) with data warehouse (Synapse) capabilities. Delta Lake provides ACID transactions on data lake storage.

Question 8: How do you design for scalability?

Answer: Serverless compute (auto-scale), partitioning (data distribution), caching (result sets), and reserved capacity (predictable growth).

Question 9: What is the medallion architecture?

Answer: Bronze (raw), Silver (cleaned), Gold (curated). Progressive data refinement with Delta Lake for ACID transactions.

Question 10: How do you design for disaster recovery?

Answer: RPO/RTO targets, geo-redundant storage, multi-region deployment, automated failover, and regular DR testing.

Question 11: What is the benefit of microservices in data engineering?

Answer: Independent deployment, scalability, fault isolation, and technology diversity. Use Azure Functions for event-driven microservices.

Question 12: How do you design for data governance?

Answer: Purview for discovery, sensitivity labels for protection, RBAC for access, business glossary for standardization, and audit logging.

Question 13: What is the benefit of infrastructure as code?

Answer: Consistent deployments, version control, reproducibility, and audit trail. Use ARM/Bicep templates or Terraform.

Question 14: How do you design for cost optimization?

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

Question 15: What is the benefit of event-driven architecture?

Answer: Loose coupling, scalability, resilience, and real-time processing. Use Event Hubs, Event Grid, and Azure Functions.

Question 16: How do you design for data quality?

Answer: Validation at ingestion, quality rules in transformation, monitoring, alerting, and quarantine for failed records.

Question 17: What is the benefit of containerization?

Answer: Consistent environments, easy deployment, scalability, and portability. Use Azure Container Instances or AKS for Spark workloads.

Question 18: How do you design for security?

Answer: Zero-trust, encryption, access control, monitoring, and compliance automation.

Question 19: What is the benefit of serverless?

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

Question 20: How do you design for multi-region deployment?

Answer: Active-active or active-passive, data replication, conflict resolution, and latency optimization.

Question 21: What is the benefit of API management?

Answer: Centralized API gateway, rate limiting, authentication, and monitoring for data services.

Question 22: How do you design for IoT workloads?

Answer: IoT Hub/Event Hubs (ingestion), Stream Analytics (processing), Cosmos DB (storage), and Synapse (analytics).

Question 23: What is the benefit of machine learning integration?

Answer: Predictive analytics, anomaly detection, and data-driven decisions. Use Azure ML with Synapse and Databricks.

Question 24: How do you design for data sharing?

Answer: Delta Sharing (Databricks), Synapse data sharing, and Power BI workspaces for controlled data access.

Question 25: What is the future of data engineering architecture?

Answer: Lakehouse convergence, real-time analytics, AI-integrated platforms, and unified analytics (Fabric).