Top 50 Microsoft Fabric Interview Questions

Microsoft Fabric is a data analytics platform unifying data engineering, data science, real-time analytics, and business intelligence in a single SaaS solution, with components as Power BI and Azure Synapse.

Explore the components of Microsoft Fabric: one lake storage, data factory, data engineering, data science, data warehouse, real-time analytics, and Power BI, with Copilot and MLflow for end-to-end data workflows.

Choose between warehouse and lakehouse architectures in fabric: warehouse for structured data, lakehouse for large, unstructured data from diverse sources, using Spark for transforms and SQL/T-SQL for reporting.

Compare Microsoft Fabric and Azure Synapse: Fabric is a SaaS platform with no infrastructure management and broad data ingestion, unlike Synapse, which remains a PaaS focused on big data analytics.

OneLake in Microsoft Fabric serves as unified storage for files. Built on Delta Lake with Parquet format, it centralizes data and enables direct integration with Power BI and Fabric components.

Discover how Microsoft Fabric secures data with Azure AD authentication, role-based and row-level access, workspace permissions, data masking, encryption, and governance via Microsoft Purview and Defender, plus Git-based deployment.

Explore lakehouse as a hybrid data structure unifying data lake and warehouse. Builds on one lake, uses delta lake format, acid compliance, and supports both data engineering and analytics.

Explore how Fabric supports data integration with Data Flow Gen2, pipelines, notebooks, One Lake, Delta Lake, and shortcuts for direct data access.

Query data directly from the lake using direct lake mode in Power BI within Fabric, avoiding movement to the Power BI data set for large data with real time updates.

Enable real-time analytics in Microsoft Fabric using event stream, KQL, and real-time dashboards. Use Azure Event Hubs, Azure IoT Hub, Kafka, and data activator to ingest, transform, and trigger alerts.

The default storage format for data in OneLake is a Delta Lake (parquet-based) format, supporting ACID transactions, schema enforcement, and versioning, with data ingestion via Scala, PySpark, and DotNet.

Enable multi-cloud integration in Fabric via shortcuts to external storage, pulling data from AWS, Google Cloud, or ADLs Gen2 without copying, while OneLake manages permissions and credentials.

discover how data activator, a no-code event-driven trigger in Microsoft Fabric, connects to live sources to automate real-time alerts, actions, and API calls.

Power BI hybrid tables blend imported historic data with direct query live data to deliver blended dashboards, faster queries, and incremental, near real time insights, supported by Fabric.

Discover how fabric handles ETL workflows, with no-code, low-code, and code options, including Azure Data Factory, data flow Gen2, notebook with Apache Spark, Delta Lake, and workload management.

Implement row-level security in fabric via power BI semantic models, defining roles and DAX filters, then publish to the fabric workspace and manage access with dynamic URL mappings.

Migrate synapse workloads to fabric by rebuilding pipelines and notebooks, migrating pipelines to fabric, moving data to one lake, and recreating the semantic model and admin security controls.

Optimize Power BI reports in fabric by tuning the data model, removing unused columns, applying star schema, incremental refresh, aggregation, and hybrid real-time history.

Automate data ingestion in fabric using pipelines, event streams, and external data access; combine data flow Gen2, notebook, and Power Automate for end-to-end integration.

Configure a data gateway to connect Fabric to an on-prem SQL Server, then use the gateway in Fabric artifacts like data flows, pipelines, and semantic models.

Fabric enables ai and machine learning through notebooks using python, pyspark, scala, and r, with distributed spark compute, ml libraries like tensorflow and pytorch, lakehouse storage, and mlflow integration.

Discover how materialized views in fabric warehouse precompute and store query results on disk, caching frequent selects to speed up repeated queries and support incremental refresh.

Compare fabric warehouse and lakehouse to clarify data types, storage, and workloads: warehouse handles structured data with t-sql for BI; lakehouse supports spark, python, and unstructured data.

Learn how OneLake shortcuts create virtual links to external data sources, enabling cross-cloud read-through access from OneLake to AWS S3 or GCP while avoiding data duplication.

Share live data from your lake house with delta sharing, an open Linux Foundation standard, enabling cross-platform access without copying data, while preserving ownership and governance with fine-grained access control.

Learn how to connect fabric with external bi tools using lakehouse or warehouse sql endpoints, odbc/jdbc, delta tables and delta sharing, including Kusto for real-time data.

Monitor Fabric workloads with built-in tools, metrics, and logs to track pipelines, data refreshes, capacity, and performance, using the central Fabric monitoring hub.

Identify the global, regional, and industry-specific compliance standards Fabric supports, including ISO and IEC information security and cloud privacy, SOC 1–3, CSA, GDPR, CCPA, FedRAMP, HIPAA, PCI DSS, and FINRA.

Explore fabric capacity and capacity units, learn how compute power, concurrency, and auto scale affect performance across Data Factory, Power BI, and Spark workloads, with SKU pricing insights.

Discover how Microsoft Purview integrates with fabric to govern catalogs, lineage, and compliance, enabling scan, catalog, metadata classification, and end-to-end data governance across assets.

Optimize queries in fabric warehouse using materialized views, star/snowflake schemas, partitioning, and columnar storage; apply query folding with data flows and power BI, and tune joins and resources.

Explore how Fabric handles schema evolution with auto detection of schema changes, Delta Lake support, and automatic schema merging to keep pipelines and queries unaffected.

Fabric offers four real-time streaming data ingestion methods—event stream, KQL-enabled SQL, real-time Power BI dashboards, and unified one lake storage—for IoT and audit log analytics.

Explore how Fabric integrates with Power Automate to trigger data changes, events, and scheduled flows. Learn use cases like threshold alerts, fraud detection, email or Teams notifications, and ETL refresh.

Understand Microsoft Fabric’s capacity-based pricing, driven by SKUs (F2, F64) across all components, with pay-as-you-go or fixed options, plus one lake storage and built-in Power BI licensing.

Discover the typical 99.9% uptime SLA for Microsoft Fabric services, noting it varies by SKU and applies to Power BI, Data Factory, and Synapse workloads.

Discover how fabric achieves high availability through cloud-native resilience, replication, automatic failover, and global infrastructure, with data redundancy, load balancing, auto-healing, and capacity-based isolation.

Explore how Microsoft Fabric supports enterprise data lake and analytics, real-time monitoring of IoT data, and customer 360 analytics, plus supply chain analytics.

Fabric enables IoT analytics by ingesting real-time sensor data via event streams, processing, storing in a lakehouse, and enabling fast queries, Power BI dashboards, and automated actions.

Explore how Fabric uses data masking, not encryption, to hide sensitive data like social security numbers while preserving queries, via dynamic masking at the warehouse and LRS/CLS for lakehouse access.

A fabric workspace is a container that organizes assets like warehouses, lake houses, datasets, pipelines, and notebooks, with team or environment categories and permissions to isolate dev, QA, and production.

Differentiate data flows and data pipelines in fabric, highlighting their use cases, tooling, and orchestration, from low-code GUI ETL with Power Query to full scripting with PySpark and Spark SQL.

Explore how fabric optimizes data freshness across real-time streaming, incremental refresh, and direct link mode, including event stream, near real-time pipelines, lake house connections, and Power BI.

Adopt domain- and environment-specific workspaces with clear naming, and integrate version control and pipelines. Optimize data models with direct query and incremental refresh, apply least privilege, and monitor costs.

Learn when to choose Direct Lake mode over import or direct query in Power BI and Microsoft Fabric, focusing on trade-offs, delta parquet reads on demand, and the behind-the-scenes fallback.