Fabric Data Engineering End-to-End Workflow: 10 practice questions

Question 1 of 10

Which sequence best represents the core Fabric data lifecycle?

1. ADeploy reports, provision separate lakes, export files, and remove governance
2. BCreate a workspace, replace all sources, disable storage, and schedule reports
3. CGet data, store it, transform it, analyze it, and deliver insights
4. DVisualize data, delete its source, create capacity, and define a tenant

Question 2 of 10

Which ingestion method references external storage without copying its data?

1. AA OneLake shortcut
2. BA scheduled data pipeline
3. CAn eventstream
4. DMirroring

Question 3 of 10

Which tool supplies a low-code Power Query transformation experience?

1. ANotebooks
2. BA semantic model
3. CDataflow Gen2
4. DA Spark job definition

Question 4 of 10

Which storage item best fits structured relational analytics with T-SQL?

1. AA Power BI semantic model
2. BA real-time eventhouse
3. CA Fabric warehouse for T-SQL analytics
4. DA transactional SQL database

Question 5 of 10

What is the primary relationship between a notebook and a pipeline in the engineering workflow?

1. AThe notebook stores every organizational file, and the pipeline replaces the OneLake layer
2. BThe notebook allocates tenant capacity, and the pipeline creates Microsoft Entra identities
3. CThe notebook promotes content to production, and the pipeline defines reporting measures
4. DThe notebook performs code-first data work, and the pipeline can orchestrate its execution

Question 6 of 10

Which ingestion option is designed for continuous replication from an operational database?

1. APower BI
2. BA shortcut
3. CA scheduled pipeline
4. DMirroring

Question 7 of 10

Where does monitoring and optimization fit in an end-to-end engineering workflow?

1. AAfter delivery and throughout operations as an ongoing feedback loop
2. BOnly inside Power BI after engineering items have been deleted
3. COutside Fabric because the platform has no operational responsibilities
4. DBefore any data source exists as a replacement for ingestion planning

Question 8 of 10

A source produces telemetry continuously. The team needs low-latency ingestion and KQL-based analysis, not scheduled batches. Which path best fits?

1. AUse an eventstream to ingest and route events into an eventhouse
2. BUse mirroring to create reports directly without an analytical data item
3. CUse a shortcut as the event processor and a semantic model as the KQL store
4. DUse a batch pipeline to copy events into a warehouse once each week

Question 9 of 10

A team must copy data nightly, apply custom code transformations, and expose the curated result through interactive reports. Which sequence is appropriate?

1. ANotebook reporting, semantic-model storage, pipeline visualization, then lakehouse ingestion
2. BPower BI ingestion, capacity transformation, shortcut modeling, then tenant reporting
3. CEventhouse ingestion, deployment-pipeline transformation, mirroring, then Spark reporting
4. DPipeline ingestion, notebook transformation, semantic modeling, then Power BI reporting

Question 10 of 10

Data already resides in supported external object storage. Engineers need Spark access without building ETL or duplicating files, followed by orchestrated notebook processing. What should they use?

1. ACreate a OneLake shortcut, then run the notebook through a pipeline
2. BCopy every file with Dataflow Gen2, then replace OneLake with a deployment pipeline
3. CMirror the files into a semantic model, then execute the model as Spark code
4. DCreate an eventstream for static files, then use Power BI to orchestrate the notebook