- 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 Kubeflow Pipelines SDK 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. A key principle to apply: kubeflow Pipelines SDK. 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: Kubeflow Pipelines SDK
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
Kubeflow Pipelines SDK
- ✗
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 mistakenly believe that the `gcloud ai pipelines` command or the `dsl.pipeline` decorator directly compiles the pipeline, but in Google's Vertex AI Pipelines, you must use the KFP SDK's `Compiler().compile()` method to generate the pipeline JSON specification.
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
- Kubeflow Pipelines SDK
- Vertex AI Pipelines
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
Kubeflow Pipelines SDK
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. Kubeflow Pipelines SDK 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.
Review kubeflow Pipelines SDK, then practise related PMLE questions on the same topic to reinforce the concept.
- →
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 — Kubeflow Pipelines SDK.
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?
Review kubeflow Pipelines SDK, then practise related PMLE questions on the same topic to reinforce the concept.
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?
Kubeflow Pipelines SDK
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Last reviewed: Jul 4, 2026
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