You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1. In Workspace1, you create a new notebook named Notebook2. You…

The high concurrency feature in Microsoft Fabric is specifically designed to allow multiple notebooks to share a single Apache Spark session. When a session is started with high concurrency enabled, subsequent notebooks from the same user can attach to this existing session instead of creating a new one. This significantly reduces the startup time for the second notebook (Notebook2) and optimizes the use of cluster resources, directly fulfilling the requirement to attach Notebook2 to the same session as Notebook1.

B. Enable dynamic allocation for the Spark pool.

Dynamic allocation manages the number of executors within a single Spark session based on workload, but it does not enable sharing the session across multiple notebooks.

C. Change the runtime version.

Changing the runtime version alters the Spark and library versions for the session but does not enable the mechanism for sharing a session between different notebooks.

D. Increase the number of executors.

Increasing executors scales the computational resources for a Spark session. This is a performance tuning measure and is unrelated to enabling session sharing.

1. Microsoft Fabric Documentation

"Get started with notebook session management in Microsoft Fabric": Under the "High-concurrency sessions" section

it states

"High-concurrency sessions create sessions that can be shared by notebooks of the same user. If you select the high-concurrency option

you can run multiple notebooks on a single session."

2. Microsoft Fabric Documentation

"Apache Spark session management in Microsoft Fabric": This document details session-level properties

including spark.ms.highConcurrency. It explains that setting this property to true enables the high-concurrency mode for session sharing.

3. Microsoft Azure Synapse Analytics Documentation

"Share Apache Spark pools in Azure Synapse Analytics": As Fabric's Spark engine is built upon Synapse

this documentation is relevant. It clarifies

"High-concurrency sessions allow multiple notebooks to run on the same session... By enabling high concurrency

you can reduce the Spark session start-up time and conserve cluster resources."