Tuning a workspace's Spark compute defaults and pool sizing
Fabric workspace admins can configure Spark settings at the workspace level to control the default runtime, pools, and environment used by all Spark jobs (notebooks, Spark job definitions, and pipeline activities) run within that workspace. These settings let organizations standardize compute behavior, manage cost via autoscaling and node sizing, and enforce a consistent Spark runtime version and library environment across items.
Must-know
- Workspace Spark settings are found under Workspace settings > Data Engineering/Science > Spark settings, and only Workspace Admins/Members with sufficient permissions can change them.
- You can select the default Spark pool for the workspace, choosing between the Fabric-managed starter pool (fast session start, autoscaling) or a custom pool with defined node family, node size, and min/max node autoscale limits.
- Workspace Spark settings let you set the default Spark runtime version (e.g., Spark 3.x runtime tied to a specific Delta Lake and Python version), and changing it affects new sessions, not already-running ones.
- You can assign a default Fabric Environment (custom libraries, Spark properties, resources) at the workspace level so notebooks and jobs inherit it automatically unless overridden at the item level.
- Workspace-level Spark configuration (Spark properties/config key-value pairs) can be set as defaults, but individual notebooks or job definitions can override them for that specific session.
- High concurrency mode and session timeout settings can be configured at the workspace level to control how sessions are shared/reused and how long idle sessions persist, impacting cost and resource utilization.
A workspace administrator wants every new notebook created in a Fabric workspace to automatically pick up a specific set of Python libraries and custom Spark configuration properties, without requiring notebook authors to manually attach anything each time. Which Spark workspace setting should the administrator configure?
What you have tried across DP-700's objectives, not a readiness score.
Implement and manage an analytics solution
- Tuning a workspace's Spark compute defaults and pool sizing
- Grouping and governing workspaces with a Fabric domain
- Setting per-workspace defaults for OneLake storage
- Standing up an Airflow job runtime inside a workspace
- Connecting a workspace to a Git repository
- Managing schema changes with a database project
- Promoting Fabric items across environments with a deployment pipeline
- Granting and restricting access at the workspace level
- Locking down who can open a single Fabric item
- Layering row, column, object, and file-level security rules
- Hiding sensitive column values behind a dynamic mask
- Classifying Fabric items with a sensitivity label
- Marking a trusted item as promoted or certified
- Reading a Fabric audit log to see who did what
- Securing data at the OneLake storage layer
- Picking the right build tool among a dataflow, a pipeline, and a notebook
- Kicking off a job on a schedule or in response to an event
- Chaining notebooks and pipelines together with parameters and dynamic expressions
Ingest and transform data
- Deciding between a full reload and an incremental load
- Shaping source data ahead of a dimensional-model load
- Landing a continuous stream of data into storage
- Matching a workload to the right Fabric data store
- Picking a transformation tool from dataflows, notebooks, KQL, or T-SQL
- Linking to external data without copying it via a OneLake shortcut
- Keeping a source database continuously replicated into Fabric
- Moving data into Fabric with a data pipeline
- Writing transform logic in PySpark, SQL, or KQL
- Flattening related tables into one wide, denormalized shape
- Rolling records up with group-by aggregations
- Dealing with duplicate rows, gaps, and data that arrives late
- Selecting the right engine for a real-time workload
- Weighing storage-in-place against a linked shortcut for a Real-Time Intelligence table
- Weighing an accelerated shortcut against a standard one for query speed
- Routing and reshaping live events with an Eventstream
- Handling a continuous flow of records with Spark's structured streaming
- Querying and reshaping event data with KQL
- Aggregating a stream over sliding or tumbling time windows
Monitor and optimize an analytics solution
- Watching an ingestion job's health and progress
- Watching a transformation job's health and progress
- Tracking whether a semantic model's refresh actually succeeded
- Setting up an alert to catch a failure early
- Tracking down why a pipeline run failed and fixing it
- Diagnosing why a dataflow run failed
- Debugging a notebook run that failed
- Troubleshooting a misbehaving Eventhouse
- Troubleshooting a misbehaving Eventstream
- Debugging a T-SQL statement that failed
- Fixing a broken or unreachable shortcut
- Speeding up a Lakehouse table with maintenance operations
- Making a slow pipeline run faster
- Tuning a Fabric warehouse for faster queries
- Improving throughput on real-time streaming components
- Tuning a Spark job to run faster and cheaper
- Making a slow query run faster
Coverage checked against the published exam guide on Aug 11, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.