- A
Dataset
Why wrong: Dataset artifact type is for datasets, not metrics.
- B
Metrics
Why wrong: Metrics is a general artifact type, but for classification metrics, the specific type is ClassificationMetrics.
- C
ClassificationMetrics
This is the correct artifact type for classification evaluation metrics.
- D
Model
Why wrong: Model artifact type is for trained models, not metrics.
Question 198 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Dataset artifact type is for datasets, not metrics.
- ✗
Metrics
Why it's wrong here
Metrics is a general artifact type, but for classification metrics, the specific type is ClassificationMetrics.
- ✓
ClassificationMetrics
Why this is correct
This is the correct artifact type for classification evaluation metrics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Model
Why it's wrong here
Model artifact type is for trained models, not metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, `ClassificationMetrics` in KFP SDK v2 is a subclass of `Metrics` that adds specific schema fields like `confusionMatrix`, `rocCurve`, and `confidenceMetrics`. When the component outputs this artifact, the KFP UI automatically renders classification-specific charts (e.g., ROC curve, confusion matrix heatmap) without custom visualization code. In real-world scenarios, using `Metrics` instead would cause the UI to display only raw scalar values, missing the rich visualizations that stakeholders expect for classification model evaluation.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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