Objective 1.1
DP-700Configure Microsoft Fabric workspace settings
Objective 1.1 sits in Implement and manage an analytics solution, which carries 33% of the DP-700 Fabric Data Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Microsoft Azure page it rests on.
Objective title verbatim from the official objectives. Microsoft Azure exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
You want executors added automatically when tasks exceed current capacity and released when jobs finish. Which pool option do you enable?
Correct.
Concept
Dynamic allocation lets the application request and release executors during a run, independent of the pool's fixed node bounds.
Why B
Dynamic allocation requests more executors under load and releases them when jobs complete.
Source
Microsoft Learn, Apache Spark compute for Data Engineering and Data Science, checked August 2026Dynamic allocation allows the Apache Spark application to request more executors if the tasks exceed the load that current executors can bear.
Now you: objective 1.1 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
Which pool gives deeper control over node size and scaling behavior for a specialized workload?
Sample question 2 of 3
When are you billed for a starter pool session?
Sample question 3 of 3
A workspace admin sets the default node size for a starter pool. Which node size do starter pools support?
That’s 3 of the full DP-700 Fabric Data Engineer bank.
Keep going free: 10 questions per certification in bank practice, with no account.
Continue practisingRead the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.