Objective 5.1

PMLE

Developing end-to-end ML pipelines

Objective 5.1 sits in Automating and orchestrating ML pipelines, which carries 18% 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.

5-1Automating and orchestrating ML pipelines

A team builds a TensorFlow workflow that processes terabytes of structured data and asks which pipelines SDK to use. Which one is recommended?

The Kubeflow Pipelines SDK, version 2 or laterThat SDK is the recommendation for other cases.
A plain Python script submitted as a custom jobA single job is not an orchestrated workflow.
TensorFlow Extended, version 0.30.0 or laterCorrect · your answerCorrect. Scale and framework both point there.
An Airflow DAG defined in a Python fileThat describes a different orchestrator entirely.

Correct.

Concept

Two SDKs describe the same execution engine, so the choice turns on the data volume and the framework the workflow already lives in rather than on what the engine is able to run.

Why C

Pipelines can run from the Kubeflow Pipelines SDK v2.0 or later, or from TensorFlow Extended v0.30.0 or later. TFX is recommended when a TensorFlow workflow processes terabytes of structured or text data, and the Kubeflow SDK is recommended for other cases.

Source

Agent Platform Pipelines can run pipelines built using any of the following SDKs: Kubeflow Pipelines SDK v2.0 or later TensorFlow Extended v0.30.0 or later If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, we recommend that you build your pipeline using TFX. To learn more about building a TFX pipeline, follow the TFX getting started tutorials . To learn more about using Agent Platform Pipelines to run a TFX pipeline, follow the TFX on Google Cloud tutorials . For other use cases, we recommend that you build your pipeline using the Kubeflow…

Build a pipeline, Agent Platform documentation, checked August 2026
#gcp#pipelines#kubeflow#tfx

Now you: objective 5.1 questions

No account needed. The explanation opens when you answer.

Sample question 1 of 3

5-1Automating and orchestrating ML pipelines

An engineer reads a pipeline definition and asks what a component actually is in that model. What does one describe?

Sample question 2 of 3

5-1Automating and orchestrating ML pipelines

A team asks in what order the steps of a compiled pipeline actually execute at run time. What decides that order?

Sample question 3 of 3

5-1Automating and orchestrating ML pipelines

A pipeline run fails to write its artifacts and the team checks the root path setting on the definition. What must that value be?

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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 Automating and orchestrating ML pipelines