What Is Microsoft Fabric?: 10 practice questions

Question 1 of 10

Which description best defines Microsoft Fabric?

1. AA desktop-only tool that creates reports from local spreadsheets
2. BA single-purpose Spark service without storage or reporting capabilities
3. CA SaaS analytics platform that supports end-to-end data workflows
4. DA physical data lake appliance installed in each business unit

Question 2 of 10

What is OneLake's platform-level role in Fabric?

1. AIt replaces every specialized workload with one universal query engine
2. BIt provides a centralized logical data lake shared by Fabric workloads
3. CIt stores only Power BI reports and excludes engineering data items
4. DIt provides a separate storage account that each notebook must provision

Question 3 of 10

Which Fabric workload is centered on Apache Spark, notebooks, and transformation jobs?

1. AData Engineering
2. BPower BI
3. CFabric Data Warehouse
4. DReal-Time Intelligence

Question 4 of 10

Which platform problem does Fabric primarily reduce?

1. AThe inability to operate any workload through cloud software
2. BThe absence of local spreadsheet formatting in business reports
3. CThe need to replace structured data with unstructured files
4. DFragmentation across disconnected data and analytics services

Question 5 of 10

How do Fabric workloads relate to one another?

1. AThey run as isolated products and require exported files for every handoff
2. BThey share a brand name but cannot use artifacts created by another workload
3. CThey remain task-specific but share a common Fabric environment and data foundation
4. DThey all expose identical tools and remove the need to select an experience

Question 6 of 10

Which sequence illustrates an end-to-end Fabric solution?

1. AStore in Copilot, transform with the catalog, and visualize with a capacity unit
2. BVisualize with OneLake, store with Power BI, and ingest with Microsoft Purview
3. CProcess with Power BI, govern with a notebook, and report through a Spark pool
4. DIngest with Data Factory, process with Data Engineering, and visualize with Power BI

Question 7 of 10

Why can OneLake reduce duplicate copies between analytics tools?

1. AFabric disables specialized storage patterns whenever more than one workload is used
2. BOneLake requires each workload to maintain a private copy under a separate tenant
3. COneLake converts every workload into the same compute engine before data is read
4. DFabric workloads can access shared data through the same logical storage layer

Question 8 of 10

A team needs scheduled ingestion, Spark transformations, and interactive business reports. It wants shared data and minimal manual integration between services. Which approach fits best?

1. AUse the three workloads, each with its own storage outside OneLake
2. BUse Data Engineering and Power BI, and load the source data by hand each morning
3. CUse Data Factory and Power BI, and run the Spark work on an outside cluster
4. DUse Data Factory, Data Engineering, and Power BI together over OneLake

Question 9 of 10

An organization has duplicated datasets across engineering, warehouse, and BI platforms. It wants specialized tools but a common governed storage foundation. What should it adopt?

1. AMicrosoft Fabric workloads operating over the tenant's single OneLake
2. BA single Fabric engine that replaces the specialized workloads
3. CSeparate platforms kept in step by nightly file transfers
4. DFabric workloads, each of them provisioning its own separate data lake

Question 10 of 10

A new analyst sees Power BI in Fabric and assumes it is the whole platform. Which correction is accurate?

1. APower BI is the workload every Fabric solution has to start from
2. BPower BI is a separate product that Fabric reaches through a connector
3. CPower BI is one specialized workload in the wider Fabric analytics platform
4. DPower BI is the workload through which all other workloads store their data