Fabric Workloads and Core Objects: 10 practice questions

Question 1 of 10

What is a Fabric workspace?

1. AA collaborative container for items that uses capacity for executed work
2. BA storage mode that reads Delta tables without importing their data
3. CA single Spark application divided into several parallel processing jobs
4. DA tenant-wide stream catalog that contains no user-created objects

Question 2 of 10

Which statement distinguishes a workload from an item?

1. AA workload is a tenant, while an item is an organizational directory
2. BA workload stores files, while an item measures compute consumption
3. CA workload groups capabilities, while an item is an object created with them
4. DA workload is a workspace role, while an item is a security principal

Question 3 of 10

What does a capacity provide in Fabric?

1. AA metadata layer of relationships and business measures
2. BA no-code stream used to route events to destinations
3. CA dedicated set of resources available to perform work
4. DA folder that contains every item created by one developer

Question 4 of 10

Which hierarchy correctly places the main organizational objects?

1. ATenant, capacity-backed workspace, then items in the workspace
2. BItem, tenant-backed notebook, then workloads inside the notebook
3. CWorkspace, item-backed tenant, then capacities inside the item
4. DCapacity unit, semantic model, then tenants inside the report

Question 5 of 10

Which object should hold structured and unstructured data for processing by Spark and SQL engines?

1. AA lakehouse
2. BA capacity unit
3. CA deployment pipeline
4. DA semantic model

Question 6 of 10

How does a Data Factory pipeline differ from a deployment pipeline?

1. AA Data Factory pipeline promotes Fabric content between environments; a deployment pipeline runs data activities.
2. BBoth pipelines move source data into storage, and deployment pipelines add the data transformations.
3. CA Data Factory pipeline runs data activities; a deployment pipeline promotes Fabric content between environments.
4. DBoth pipelines promote content between environments, and Data Factory pipelines add version control.

Question 7 of 10

What is the role of a OneLake shortcut?

1. AReference another file-store location without copying the data
2. BDefine relationships and measures for interactive business reports
3. CPackage Spark parameters for a scheduled batch application
4. DAllocate capacity units to every item in a selected workspace

Question 8 of 10

An engineer will explore the data cell by cell, keep the charts next to the code, and hand the same artifact to a nightly schedule later. Which Data Engineering item should the work start in?

1. AA Spark job definition, submitted with fixed parameters from the start
2. BA notebook that can be used as a pipeline activity
3. CAn environment, configured with the libraries the exploration needs
4. DA semantic model, refreshed on the same nightly schedule

Question 9 of 10

An administrator needs collaboration boundaries, access controls, and a place to group lakehouses and notebooks. Compute for their operations must come from assigned resources. What should be configured?

1. AA workspace assigned to Fabric capacity
2. BA shortcut assigned to a real-time data stream
3. CA Spark job definition assigned to a notebook cell
4. DA semantic model assigned to a deployment pipeline

Question 10 of 10

A solution must transform data with a visual Power Query interface and then coordinate movement through multiple activities. Which two objects satisfy those distinct needs?

1. AA deployment pipeline for transformation and a shortcut for activity sequencing
2. BA tenant for transformation and a lakehouse folder for orchestration
3. CDataflow Gen2 for transformation and a Data Factory pipeline for orchestration
4. DA semantic model for transformation and a capacity unit for orchestration