The correct answer is to create a custom component using the Vertex AI Pipelines SDK `@component` decorator. This decorator transforms a standard Python function into a reusable pipeline component by automatically packaging the code into a container image, generating the necessary component specification, and integrating it with the pipeline orchestration engine. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of how to extend Vertex AI Pipelines beyond prebuilt components, often appearing in scenarios requiring custom validation or business logic. A common trap is assuming you must manually build a Docker image or use a separate container registry, but the SDK handles all of that for you. Memory tip: think of `@component` as the "auto-pilot" for custom code—it wraps your function into a fully managed, containerized component without any manual infrastructure work.
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 company uses Vertex AI Pipelines with prebuilt components for data processing, training, and deployment. They need to integrate a custom validation step written in Python. What is the correct way to include this as a component?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a custom component using the Vertex AI Pipelines SDK @component decorator
Option C is correct because the Vertex AI Pipelines SDK provides a `@component` decorator that allows you to define a custom Python function as a pipeline component. This decorator automatically handles packaging the Python code into a container image, generating the component specification, and integrating it seamlessly with the pipeline orchestration engine. It is the idiomatic and recommended way to add custom validation logic without manually managing Docker or infrastructure.
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.
✗
Package the code in a Docker container and reference it as a custom job
Why it's wrong here
More work than needed; SDK component is simpler.
✗
Define the step in the YAML pipeline definition using arbitrary Python commands
Why it's wrong here
Not supported; must be a component.
✓
Create a custom component using the Vertex AI Pipelines SDK @component decorator
Why this is correct
Standard method for custom components.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a Cloud Function as a pipeline step
Why it's wrong here
Not directly supported.
✗
Write a standalone Python script and call it using a Cloud Shell step
Why it's wrong here
Not a component; Cloud Shell not part of pipeline.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the `@component` decorator with a simple function wrapper and assume they can just write inline Python code in the pipeline YAML (Option B), not realizing that Vertex AI Pipelines requires each step to be a containerized component with explicit input/output definitions.
Detailed technical explanation
How to think about this question
The `@component` decorator in the Vertex AI Pipelines SDK (part of the `google_cloud_pipeline_components` library) uses KFP (Kubeflow Pipelines) under the hood to generate a containerized component. It automatically infers input/output types, builds a Docker image using a base Python image, and registers the component in the pipeline metadata store. A subtle behavior is that the decorator can optionally accept a `base_image` parameter to specify a custom environment, which is critical when the validation step requires specific system libraries or Python packages not included in the default image.
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.
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: Create a custom component using the Vertex AI Pipelines SDK @component decorator — Option C is correct because the Vertex AI Pipelines SDK provides a `@component` decorator that allows you to define a custom Python function as a pipeline component. This decorator automatically handles packaging the Python code into a container image, generating the component specification, and integrating it seamlessly with the pipeline orchestration engine. It is the idiomatic and recommended way to add custom validation logic without manually managing Docker or infrastructure.
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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Question Discussion
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