Objective 2.2

PMLE

Model prototyping using notebooks

Objective 2.2 sits in Collaborating within and across teams to manage data and models, which carries 16% of the ML Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.

Objective title verbatim from the official objectives. Google Cloud exam page

A worked example

Shown solved, with the whole explanation open: this is what every question here carries.

2-2Collaborating within and across teams to manage data and models

A finance lead asks why managed notebook instances stop on their own overnight with nobody acting on them. What is the default behaviour?

Instances run continuously until stopped by handRunning on forever is the setting you must select.
Instances restart nightly on a fixed maintenance windowNo maintenance restart schedule governs these machines.
Instances shut down after a set idle periodCorrect · your answerCorrect. Inactivity is what triggers the stop.
Instances suspend when their disk quota is reachedStorage limits do not drive the lifecycle here.

Correct.

Concept

An interactive workspace bills for the machine rather than for the work, so idle time is pure cost. Making the shutdown a default rather than an option is how a platform stops that cost accruing by accident.

Why C

To help manage costs, notebook instances shut down after being idle for a specific time period by default. The amount of time can be changed, and the feature can also be turned off.

Source

Automated shutdown for idle instances To help manage costs, Agent Platform Workbench instances shut down after being idle for a specific time period by default. You can change the amount of time or turn this feature off.

Introduction to Workbench instances, Agent Platform documentation, checked August 2026
#gcp#notebooks#cost#workbench

Now you: objective 2.2 questions

No account needed. The explanation opens when you answer.

Sample question 1 of 3

2-2Collaborating within and across teams to manage data and models

A team needs an older TensorFlow release and an R kernel on a notebook instance that ships with neither of them. What supplies both?

Sample question 2 of 3

2-2Collaborating within and across teams to manage data and models

An engineer plans to build a notebook instance image starting from an unrelated public Python base image. What does the guidance say about that?

Sample question 3 of 3

2-2Collaborating within and across teams to manage data and models

A team runs notebook instances inside a service perimeter and plans to schedule notebook runs from them. Which limitation applies there?

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Read 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.

More objectives in Collaborating within and across teams to manage data and models