- A
Write each component as a Cloud Composer DAG task using Python operators and manage dependencies via Airflow.
Why wrong: Cloud Composer orchestrates pipelines but doesn't provide the typed artifact management that KFP components do.
- B
Use Vertex AI pre-built components exclusively and chain them using the Vertex AI SDK without a pipeline definition.
Why wrong: Pre-built components are useful but limited; custom logic requires Python function components, and chaining without a pipeline definition is not possible.
- C
Define each step as a separate Cloud Build step and chain them via build triggers.
Why wrong: Cloud Build is for CI/CD builds, not for defining ML pipeline components with typed artifact passing.
- D
Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.
This is the standard approach for reusable, composable ML pipeline components on Vertex AI Pipelines.
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 data science team wants to build a machine learning pipeline on Vertex AI Pipelines that preprocesses data, trains a model, and evaluates it. They need to ensure that components can be reused across multiple pipelines and that outputs from one component can be passed as inputs to another. Which approach should they take?
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
Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.
Option D is correct because Kubeflow Pipelines SDK v2 with @dsl.component and @dsl.pipeline decorators is the native way to define reusable, composable components in Vertex AI Pipelines. This approach allows each component to be a self-contained Python function that can be independently versioned and reused across multiple pipelines, with outputs automatically serialized and passed as inputs to downstream components via the pipeline graph.
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.
- ✗
Write each component as a Cloud Composer DAG task using Python operators and manage dependencies via Airflow.
Why it's wrong here
Cloud Composer orchestrates pipelines but doesn't provide the typed artifact management that KFP components do.
- ✗
Use Vertex AI pre-built components exclusively and chain them using the Vertex AI SDK without a pipeline definition.
Why it's wrong here
Pre-built components are useful but limited; custom logic requires Python function components, and chaining without a pipeline definition is not possible.
- ✗
Define each step as a separate Cloud Build step and chain them via build triggers.
Why it's wrong here
Cloud Build is for CI/CD builds, not for defining ML pipeline components with typed artifact passing.
- ✓
Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline.
Why this is correct
This is the standard approach for reusable, composable ML pipeline components on Vertex AI Pipelines.
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 misconception that any orchestration tool (Airflow, Cloud Build) can substitute for a purpose-built ML pipeline framework, but the key differentiator is Vertex AI Pipelines' native support for reusable components with typed artifact passing and managed execution.
Detailed technical explanation
How to think about this question
Under the hood, @dsl.component creates a containerized component by auto-generating a Docker image from the Python function, with inputs and outputs defined as typed parameters or artifacts (e.g., Dataset, Model). The @dsl.pipeline decorator compiles the component graph into an IR (Intermediate Representation) YAML that Vertex AI Pipelines executes as a managed orchestration DAG, handling data passing via Cloud Storage URIs and ML Metadata tracking. In a real-world scenario, a team can define a shared 'preprocess' component once and reuse it in training, inference, and retraining pipelines without code duplication, while Vertex AI automatically handles caching of component outputs for faster iterative development.
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 — study guide chapter
Learn the concepts, then practise the questions
- →
Automating and Orchestrating ML Pipelines practice questions
Targeted practice on this topic area only
- →
All PMLE questions
1,000 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Use Kubeflow Pipelines SDK v2 to create Python function components decorated with @dsl.component and compose them into a pipeline using @dsl.pipeline. — Option D is correct because Kubeflow Pipelines SDK v2 with @dsl.component and @dsl.pipeline decorators is the native way to define reusable, composable components in Vertex AI Pipelines. This approach allows each component to be a self-contained Python function that can be independently versioned and reused across multiple pipelines, with outputs automatically serialized and passed as inputs to downstream components via the pipeline graph.
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
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.