PMLE Automating and Orchestrating ML Pipelines Practice Question
An ML engineer is building a pipeline component that takes a dataset URI and a model URI as inputs, and outputs a classification metrics artifact. Which KFP SDK v2 type should the output artifact be annotated with?
⚠ Common exam trap
Many exam-takers confuse the generic `Metrics` type (which handles scalar values) with the specialized `ClassificationMetrics` type, not realizing that KFP SDK v2 requires the specific artifact type to enable proper UI rendering and schema validation for classification outputs.
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
✓
ClassificationMetrics
In KFP SDK v2, the `ClassificationMetrics` type is specifically designed to output classification metrics such as confusion matrix, ROC curve, and AUC. The question asks for a component that outputs classification metrics, so `ClassificationMetrics` is the correct artifact type. Using `Metrics` would be too generic and not provide the structured schema needed for classification-specific visualizations in the KFP UI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataset
Why it's wrong here
A Dataset artifact represents input data such as tables or files, not evaluation results. The component outputs classification metrics, so a Dataset annotation mislabels the artifact and prevents metric-specific handling. Dataset is correct for components that emit prepared training or test data.
- ✗
Metrics
Why it's wrong here
Metrics is a KFP v2 system artifact type for scalar metric values, not for a classification metrics report, so annotating the output with it misrepresents the artifact. It is tempting because the output is metrics, but the correct annotation is ClassificationMetrics, which carries structured confusion-matrix and per-class data.
- ✓
ClassificationMetrics
Why this is correct
ClassificationMetrics is the KFP v2 artifact type purpose-built for classification evaluation output, carrying fields such as confusion matrix, precision and recall. Annotating the output with it satisfies the stem's requirement for a classification metrics artifact.
- ✗
Model
Why it's wrong here
A Model artifact represents trained model weights and metadata, not evaluation output. The component emits classification metrics, so annotating the output as Model misdescribes its content and breaks downstream consumers expecting metric values. Model suits components that produce a deployable trained model.
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