MS Fabric Architect Interview Questions

Q1. What is Microsoft Fabric?
Unified SaaS data platform combining Data Engineering, Data Science, Data Warehouse, Real-Time Analytics, and Power BI
Built on OneLake (single data lake)
Eliminates need for multiple services like ADF, Synapse, Power BI separately

Q2. What is OneLake?
Central storage layer (like OneDrive for data)
Uses Delta Lake format
Supports shortcuts (no data duplication)

Q3. Difference between Microsoft Fabric and Azure Data Factory?
ADF → orchestration + pipelines only
Fabric → end-to-end platform (storage + compute + BI)
Fabric pipelines ≈ ADF but tightly integrated with lakehouse
🔹 2. Architecture & Design

Q4. How do you design a scalable Fabric architecture for 1000+ customers?
Expected points:
Workspace strategy (per customer vs domain-based)
Capacity planning (F SKU sizing)
Data isolation (schemas, folders, lakehouses)
Use of shortcuts for shared datasets
Governance via Purview

Q5. What are Lakehouse and Warehouse in Fabric?
Lakehouse
Files + tables (Delta format)
Good for data engineering & ML
Warehouse
SQL-based analytics
Optimized for BI queries

Q6. When would you use Lakehouse vs Warehouse?
Lakehouse → ingestion, transformation, ML
Warehouse → reporting, star schema, Power BI

🔹 3. Data Engineering & Pipelines
Q7. How do Fabric Data Pipelines differ from ADF pipelines?
Similar UI and activities
Fabric pipelines are tightly integrated with OneLake
No need for separate IR (mostly)
Better native support for Lakehouse

Q8. Explain incremental load strategies in Fabric.
You already know this—expect follow-ups:
Watermark (last run timestamp)
CDC (Change Data Capture)
Delta table merge
Using Copy Activity with filters

Q9. How do you implement pagination in Fabric pipelines?
(They may expect something like your ESRI API scenario)
Use Until loop
Maintain offset variable
Call API using Copy/Web activity
Append to Lakehouse table

🔹 4. Delta Lake & Data Modeling
Q10. What is Delta Lake and why is it important in Fabric?
ACID transactions
Time travel
Schema evolution
Supports incremental loads

Q11. How do you handle slowly changing dimensions (SCD) in Fabric?
Use MERGE in Delta tables
dbt snapshots (if using dbt)
Maintain valid_from, valid_to

Q12. Bronze, Silver, Gold architecture in Fabric?
Bronze → raw ingestion
Silver → cleaned/transformed
Gold → business-ready

🔹 5. Performance Optimization
Q13. How do you optimize performance in Fabric Lakehouse?
Partitioning (date/customer)
Z-ordering
File size optimization (avoid small files)
Caching

Q14. What is shortcut in OneLake and when to use it?
Reference external data without copying
Useful for multi-workspace sharing

🔹 6. Security & Governance
Q15. How do you secure data in Fabric?
Workspace-level access
Row-level security (Power BI)
Object-level security
Integration with Purview

Q16. How do you manage multi-tenant data securely?
Separate workspaces OR schemas
Use RBAC
Data masking

🔹 7. Real-Time & Advanced Topics
Q17. What is Real-Time Analytics in Fabric?
Event streams + KQL database
Used for IoT/log analytics

Q18. How would you design IoT data ingestion in Fabric?
Event streaming → KQL DB
Store raw in Lakehouse
Transform to Delta tables
Serve via Power BI

🔹 8. Scenario-Based Questions (VERY IMPORTANT)
Q19. A client has 700+ customers and 1000+ workspaces. How would you optimize?
Consolidate workspaces (domain-based)
Use shortcuts instead of duplication
Central governance
Capacity optimization

Q20. API data ingestion with pagination and failure handling?
Until loop
Retry logic
Logging table
Idempotent loads

Q21. How do you handle data quality in Fabric?
DQ rules table (like your setup)
PySpark validation
Separate failed records
Monitoring dashboards

🔹 9. Integration with Other Tools
Q22. How does Fabric integrate with dbt?
Use dbt with Lakehouse/Warehouse
dbt models for transformation
dbt snapshots for SCD

Q23. Can Fabric replace Snowflake?
Depends:
Fabric → unified + cheaper (in some cases)
Snowflake → mature + strong performance
Many orgs use hybrid

🔹 10. Trick / Deep Questions
Q24. What are limitations of Fabric?
Still evolving
Some enterprise features missing vs Synapse/Snowflake
Capacity-based pricing challenges
Q25. How does compute work in Fabric?
Capacity-based (F SKUs)
Shared compute across workloads
🔥 How to Prepare Smartly (Based on your profile)
You already worked on:
Pagination pipelines
Incremental load
dbt + Snowflake
👉 So focus on:
Mapping your experience → Fabric concepts
Scenario-based answers (interviewers LOVE this)
Architecture decisions (not just features)
If you want, I can:
Do a mock interview (real questions + feedback)
Or give answers to these questions in detail (architect-level)
Or create Fabric architecture diagrams for interviews