- A
Importer component
Why wrong: Importer is used to bring external artifacts into the pipeline, not for running custom code.
- B
Python function component with @dsl.component
This decorator turns a Python function into a pipeline component without requiring a custom container.
- C
Container component
Why wrong: Container components require a pre-built container image, which is more complex than needed for simple Python code.
- D
Vertex AI Training job
Why wrong: Vertex AI Training is a managed service for model training, not for defining lightweight pipeline components.
Question 999 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 wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they 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
✓
Python function component with @dsl.component
The `@dsl.component` decorator in KFP SDK allows you to define a lightweight Python function component that runs custom code without requiring a container image. It automatically generates a container specification from the function's dependencies, making it ideal for simple, non-containerized pipeline steps.
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.
- ✗
Importer component
Why it's wrong here
Importer is used to bring external artifacts into the pipeline, not for running custom code.
- ✓
Python function component with @dsl.component
Why this is correct
This decorator turns a Python function into a pipeline component without requiring a custom container.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Container component
Why it's wrong here
Container components require a pre-built container image, which is more complex than needed for simple Python code.
- ✗
Vertex AI Training job
Why it's wrong here
Vertex AI Training is a managed service for model training, not for defining lightweight pipeline components.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'lightweight' with 'no container at all,' but KFP always runs components in containers; the `@dsl.component` feature automates container creation, not eliminates it.
Detailed technical explanation
How to think about this question
Under the hood, `@dsl.component` uses the KFP SDK's `create_component_from_func` mechanism to introspect the function's source code, dependencies, and type annotations, then generates a container spec that includes a base Python image (e.g., `python:3.9`) and installs required packages. This avoids the need for manual Dockerfile creation and image building, but note that the function's code is still executed inside a container—the SDK handles the containerization transparently. A real-world scenario is rapid prototyping where you want to test a custom data transformation step without the overhead of building and pushing a container image to a registry.
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: Python function component with @dsl.component — The `@dsl.component` decorator in KFP SDK allows you to define a lightweight Python function component that runs custom code without requiring a container image. It automatically generates a container specification from the function's dependencies, making it ideal for simple, non-containerized pipeline steps.
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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