PMLE Automating and Orchestrating ML Pipelines Practice Question
A team develops a pipeline that trains a model and evaluates it. They want to pass the test accuracy (a float) from the evaluation component to a subsequent deployment component. Which KFP SDK type should the evaluation component output be annotated with?
⚠ Common exam trap
PMLE often tests the distinction between KFP parameter types (Output[float], Output[int], Output[str]) and artifact types (Output[Metrics], Output[Artifact], Output[ClassificationMetrics]), catching candidates who assume any numeric output should be a Metrics artifact.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Output[float]
In the KFP SDK, a component output annotated as Output[float] is treated as a lightweight scalar parameter that is passed directly between components via the pipeline's execution graph. Since test accuracy is a single floating-point value (not a file, dataset, or structured metric object), Output[float] is the correct type to declare so the downstream deployment component can consume it as a typed input. KFP serializes this primitive and makes it available for parameter passing without needing artifact storage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Output[float]
Why this is correct
Output[float] annotates the component's return value as a scalar float, which is exactly the test accuracy type the deployment component consumes. KFP serialises this primitive and passes it as a typed input, avoiding string parsing or artifact handling for a simple numeric metric.
- ✗
Output[Metrics]
Why it's wrong here
Output[Metrics] writes scalar values into ML Metadata for visualisation, not into the pipeline graph as a consumable parameter, so the deployment component cannot read the accuracy as an input. It suits recording training curves for comparison in the UI, not passing a float between components.
- ✗
Output[Artifact]
Why it's wrong here
Output[Artifact] emits a file or directory reference, so the deployment component receives a path rather than the float value itself. It is correct when passing models, datasets or evaluation reports between components, not a single scalar such as test accuracy.
- ✗
Output[ClassificationMetrics]
Why it's wrong here
Output[ClassificationMetrics] produces confusion matrices, ROC curves and threshold tables for visualisation, not a single float consumable downstream. The deployment component cannot accept it as a scalar input. It is correct when logging per-class evaluation plots for review in the KFP UI.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.