Study plan
Every objective in the published exam guide, grouped by exam section and ordered the way the guide orders them.
0 answered correctly0 not correct, still open54 not tried yet0 of 54 objectives tried
This is a record of what you have tried, not a score and not a prediction of the real exam.
Start hereStudy noteImplement and manage an analytics solution
Tuning a workspace's Spark compute defaults and pool sizing
Objective 1 of 18 in Implement and manage an analytics solution, the first section in the guide. Not tried yet. See all 3 exam sections
What this note covers
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.
The facts the exam tests
- 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?
The 3 exam sections
Ordered as the guide orders them
Implement and manage an analytics solution30-35% of the exam0 of 18 tried
Ingest and transform data30-35% of the exam0 of 19 tried
Monitor and optimize an analytics solution30-35% of the exam0 of 17 tried
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.