- A
@dsl.task
Why wrong: @dsl.task is not a valid decorator in KFP SDK v2.
- B
@dsl.pipeline
Why wrong: @dsl.pipeline is for defining a pipeline, not a component.
- C
@dsl.component
Correct decorator for defining a Python function component with typed inputs/outputs.
- D
@dsl.container
Why wrong: @dsl.container is for container components that wrap a Docker image.
Question 178 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.
A machine learning engineer is using Vertex AI Pipelines and wants to run a custom Python function as a component. They need to pass a dataset artifact from a previous component and output a model artifact. Which decorator should they use to define the component in the Kubeflow Pipelines SDK v2?
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
✓
@dsl.component
The correct decorator is @dsl.component because in Kubeflow Pipelines SDK v2, this decorator is used to define a custom Python function as a reusable pipeline component. It automatically handles input and output artifact serialization, such as passing a dataset artifact from a previous component and outputting a model artifact, by leveraging the component's type annotations and the KFP artifact system.
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.
- ✗
@dsl.task
Why it's wrong here
@dsl.task is not a valid decorator in KFP SDK v2.
- ✗
@dsl.pipeline
Why it's wrong here
@dsl.pipeline is for defining a pipeline, not a component.
- ✓
@dsl.component
Why this is correct
Correct decorator for defining a Python function component with typed inputs/outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
@dsl.container
Why it's wrong here
@dsl.container is for container components that wrap a Docker image.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse @dsl.component (for custom Python functions with artifact I/O) with @dsl.container (for pre-built container images) when the question emphasizes running a custom Python function.
Detailed technical explanation
How to think about this question
Under the hood, @dsl.component wraps the Python function into a KFP component that generates a container specification and serializes inputs/outputs as Cloud Storage URIs via the Artifact Registry. A subtle behavior is that the function must use type hints like 'Input[Dataset]' and 'Output[Model]' to enable automatic artifact passing, and the decorator compiles the function into a self-contained Docker image at pipeline submission time. In real-world scenarios, this allows teams to reuse Python logic across pipelines without managing container builds manually.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Automating and Orchestrating ML Pipelines — study guide chapter
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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: @dsl.component — The correct decorator is @dsl.component because in Kubeflow Pipelines SDK v2, this decorator is used to define a custom Python function as a reusable pipeline component. It automatically handles input and output artifact serialization, such as passing a dataset artifact from a previous component and outputting a model artifact, by leveraging the component's type annotations and the KFP artifact system.
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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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