- A
kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json')
This is the correct command to compile a pipeline function to JSON.
- B
gcloud ai pipelines compile command.
Why wrong: There is no such gcloud command; compilation is done via kfp SDK.
- C
kfp.Client().upload_pipeline()
Why wrong: This uploads a pipeline, but does not compile to JSON.
- D
dsl.pipeline decorator automatically compiles at runtime.
Why wrong: The decorator does not compile; explicit compile is needed.
Question 717 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.
An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"which command"Why it matters: Tests specific CLI syntax. Recall the exact command and its required context — near-synonyms and partial matches are common distractors.
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
✓
kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json')
Option A is correct because the Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.
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.
- ✓
kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json')
Why this is correct
This is the correct command to compile a pipeline function to JSON.
Clue confirmation
The clue word "which command" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
gcloud ai pipelines compile command.
Why it's wrong here
There is no such gcloud command; compilation is done via kfp SDK.
- ✗
kfp.Client().upload_pipeline()
Why it's wrong here
This uploads a pipeline, but does not compile to JSON.
- ✗
dsl.pipeline decorator automatically compiles at runtime.
Why it's wrong here
The decorator does not compile; explicit compile is needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the compilation step with pipeline submission or runtime execution, mistakenly thinking that the `dsl.pipeline` decorator or `gcloud` commands handle compilation automatically, when in fact the KFP SDK's `Compiler().compile()` is the explicit and required method for generating the JSON pipeline definition.
Trap categories for this question
Command / output trap
There is no such gcloud command; compilation is done via kfp SDK.
Detailed technical explanation
How to think about this question
Under the hood, `kfp.compiler.Compiler().compile()` traverses the decorated pipeline function, resolves component references (including containerized components and lightweight Python components), and serializes the execution graph into a JSON structure conforming to the PipelineSpec protobuf schema. This JSON includes metadata such as component definitions, input/output parameters, and dependency edges, which Vertex AI Pipelines interprets to orchestrate containerized steps on AI Platform. A subtle behavior is that the compiler also handles type coercion and schema validation for component inputs/outputs, ensuring compatibility with Vertex AI's runtime environment.
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: kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json') — Option A is correct because the Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.
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.
Are there clue words in this question I should notice?
Yes — watch for: "which command". Tests specific CLI syntax. Recall the exact command and its required context — near-synonyms and partial matches are common distractors.
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.
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