- A
Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.
Correct: Parameters are function arguments of the pipeline function.
- B
Pipeline parameters must be serialized to JSON before use.
Why wrong: Parameters are natively passed as typed values; no manual serialization needed.
- C
Pipeline parameters can only be of type str.
Why wrong: They can be str, int, float, bool, dict, list, etc.
- D
Pipeline parameters can be overridden at pipeline run time.
Correct: When triggering a pipeline run, you can provide new parameter values.
- E
Pipeline parameters can be used to pass large datasets between components.
Why wrong: Large datasets should use artifacts, not parameters.
Question 42 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 scientist is creating a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. Which TWO statements about pipeline parameters are correct? (Choose two.)
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
✓
Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.
Option A is correct because in the Kubeflow Pipelines SDK v2, pipeline parameters are explicitly defined as input arguments to the pipeline function that is decorated with @dsl.pipeline. These parameters serve as the primary mechanism for passing configuration values (e.g., model name, learning rate, number of epochs) into the pipeline at creation time and can be consumed by downstream components.
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.
- ✓
Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline.
Why this is correct
Correct: Parameters are function arguments of the pipeline function.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Pipeline parameters must be serialized to JSON before use.
Why it's wrong here
Parameters are natively passed as typed values; no manual serialization needed.
- ✗
Pipeline parameters can only be of type str.
Why it's wrong here
They can be str, int, float, bool, dict, list, etc.
- ✓
Pipeline parameters can be overridden at pipeline run time.
Why this is correct
Correct: When triggering a pipeline run, you can provide new parameter values.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Pipeline parameters can be used to pass large datasets between components.
Why it's wrong here
Large datasets should use artifacts, not parameters.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates often assume pipeline parameters must be JSON-serialized or limited to strings due to older Kubeflow v1 conventions, but Vertex AI's Kubeflow Pipelines SDK v2 natively supports multiple Python types and automatic serialization.
Detailed technical explanation
How to think about this question
Under the hood, the Kubeflow Pipelines SDK v2 compiles the pipeline function into an intermediate representation (IR) where parameters are serialized as part of the pipeline spec. At runtime, parameters can be overridden via the Vertex AI console, API, or CLI using the 'parameter_values' argument, which injects new values into the execution graph without recompiling the pipeline. This is particularly useful for A/B testing or hyperparameter tuning scenarios where you want to reuse the same pipeline definition with different inputs.
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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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: Pipeline parameters are defined as inputs to the pipeline function decorated with @dsl.pipeline. — Option A is correct because in the Kubeflow Pipelines SDK v2, pipeline parameters are explicitly defined as input arguments to the pipeline function that is decorated with @dsl.pipeline. These parameters serve as the primary mechanism for passing configuration values (e.g., model name, learning rate, number of epochs) into the pipeline at creation time and can be consumed by downstream components.
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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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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