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 OneLake as the transformation engine and remove every task-specific workload
2. BDeploy three isolated services and export a full data copy after every processing step
3. CUse Power BI alone and replace ingestion and Spark processing with report visuals
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 OneLake
2. BIndependent platforms with separate storage and manual file transfers
3. CA collection of local notebooks without tenant-wide storage or governance
4. DOne universal reporting workload replacing engineering and warehouse processing

Question 10 of 10

A new engineer sees a notebook inside a workspace and assumes it is the whole Fabric platform. Which correction is accurate and useful for navigating the product?

1. AThe notebook is OneLake, because code cells provide the platform's shared storage layer
2. BThe notebook is the tenant, and every other workload must be created inside that document
3. CThe notebook is an item in Data Engineering, which is one workload in the wider Fabric platform
4. DThe notebook is a capacity unit, so it defines how much compute every workspace receives