Fabric Analytics 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

A team needs curated data for analytical reporting rather than transaction processing, queried with set-based relational statements over defined schemas. Which Fabric storage item fits?

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

Question 5 of 10

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

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

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 do governance and lifecycle management fit in an end-to-end Fabric analytics workflow?

1. AOnly before a source is connected, after which governance no longer applies
2. BOutside Fabric because the platform has no governance or development integration
3. COnly inside Power BI after every storage item has been removed
4. DAcross the data journey and after delivery as continuing responsibilities

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 an eventstream to route the events into a warehouse for KQL analysis
3. CUse a shortcut to the stream and query it with KQL
4. DUse mirroring to replicate the source into an eventhouse for KQL analysis

Question 9 of 10

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

1. AEventstream ingestion, notebook transformation, semantic modeling, then Power BI reporting
2. BPipeline ingestion, Dataflow Gen2 transformation, semantic modeling, then Power BI reporting
3. CPipeline ingestion, notebook transformation, then Power BI reporting with no model
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 access without building ETL or duplicating files, followed by code-first transformation. What should they use?

1. ACopy the files with a scheduled pipeline, then transform them in a notebook
2. BMirror the external storage, then transform the replica in a notebook
3. CCreate a OneLake shortcut, then transform the referenced data in a notebook
4. DCreate a OneLake shortcut, then transform the referenced data in Dataflow Gen2