- A
@dsl.task
Why wrong: Not a valid decorator in KFP SDK v2.
- B
@component
Why wrong: This is from the older KFP SDK v1.
- C
@dsl.pipeline
Why wrong: This is for defining a pipeline, not a component.
- D
@dsl.component
Correct decorator for a component in KFP SDK v2.
Question 756 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.
You are defining a Python function component in KFP SDK v2. Which decorator should you use?
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
In KFP SDK v2, the `@dsl.component` decorator is used to define a Python function as a lightweight, reusable pipeline component that can be executed independently. This decorator automatically generates a containerized component from the function's signature and type annotations, enabling type-safe inputs and outputs without requiring a separate component YAML specification.
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
Not a valid decorator in KFP SDK v2.
- ✗
@component
Why it's wrong here
This is from the older KFP SDK v1.
- ✗
@dsl.pipeline
Why it's wrong here
This is for defining a pipeline, not a component.
- ✓
@dsl.component
Why this is correct
Correct decorator for a component in KFP SDK v2.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between v1 and v2 decorators, so the trap here is that candidates familiar with KFP SDK v1 may incorrectly choose `@component` (option B) instead of the v2-specific `@dsl.component`.
Detailed technical explanation
How to think about this question
Under the hood, `@dsl.component` leverages Python type hints to infer the component's input/output schema and automatically generates a container spec using the base image `python:3.9` (or a custom image if specified). A subtle behavior is that the decorated function must be a pure Python function with no side effects outside its scope, as the component runs in an isolated container; any external dependencies must be declared via the `packages_to_install` parameter. In real-world scenarios, this decorator is critical for building modular, testable ML pipeline steps that can be reused across different pipelines without manual containerization.
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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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 — In KFP SDK v2, the `@dsl.component` decorator is used to define a Python function as a lightweight, reusable pipeline component that can be executed independently. This decorator automatically generates a containerized component from the function's signature and type annotations, enabling type-safe inputs and outputs without requiring a separate component YAML specification.
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.
About these practice questions
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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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