- A
@dsl.component\ndef my_component(input_text: str) -> Metric:\n metrics = Metric()\n metrics.log_metric('length', len(input_text))
Why wrong: Incorrect artifact type: should be Metrics (plural) and base_image missing; also Metric class doesn't exist in kfp.dsl.
- B
@dsl.pipeline\ndef my_pipeline(input_text: str):\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))
Why wrong: Incorrect: @dsl.pipeline is for pipeline definitions, not components. Also missing return type.
- C
def my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics
Why wrong: Missing @dsl.component decorator; without it, this is a plain Python function, not a KFP component.
- D
@dsl.component(base_image='python:3.9')\ndef my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics
Correct: Uses @dsl.component with base_image, imports Metrics inside the function, and returns a Metrics artifact.
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 scientist creates a custom Python function component for a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. The component takes a string parameter 'input_text' and outputs a Metrics artifact. The scientist wants to include a lightweight Python function without building a container. Which code snippet correctly defines this component?
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
@dsl.component(base_image='python:3.9')\ndef my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics
Option D is correct because it uses the `@dsl.component` decorator with a `base_image` parameter, which is required for lightweight Python function components in Kubeflow Pipelines SDK v2. The decorator enables the component to run without a custom container by specifying a base image (here, `python:3.9`), and the function correctly returns a `Metrics` artifact after logging a metric. Without the decorator or with an incorrect decorator, the component would not be recognized as a pipeline component.
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.
- ✗
@dsl.component\ndef my_component(input_text: str) -> Metric:\n metrics = Metric()\n metrics.log_metric('length', len(input_text))
Why it's wrong here
Incorrect artifact type: should be Metrics (plural) and base_image missing; also Metric class doesn't exist in kfp.dsl.
- ✗
@dsl.pipeline\ndef my_pipeline(input_text: str):\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))
Why it's wrong here
Incorrect: @dsl.pipeline is for pipeline definitions, not components. Also missing return type.
- ✗
def my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics
Why it's wrong here
Missing @dsl.component decorator; without it, this is a plain Python function, not a KFP component.
- ✓
@dsl.component(base_image='python:3.9')\ndef my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics
Why this is correct
Correct: Uses @dsl.component with base_image, imports Metrics inside the function, and returns a Metrics artifact.
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 requirement for the `base_image` parameter in `@dsl.component` for lightweight Python functions, and candidates mistakenly assume the decorator alone is sufficient without specifying the base image.
Detailed technical explanation
How to think about this question
Under the hood, the `@dsl.component` decorator with `base_image` compiles the Python function into a containerized component using the specified image, automatically handling serialization and deserialization of inputs/outputs. The `Metrics` artifact is a special output type that allows logging metrics during pipeline execution, which are then tracked in Vertex AI Experiments. A subtle behavior is that the `base_image` must be a publicly available container image with Python installed; otherwise, the component will fail at runtime.
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: @dsl.component(base_image='python:3.9')\ndef my_component(input_text: str) -> Metrics:\n from kfp.dsl import Metrics\n metrics = Metrics()\n metrics.log_metric('length', len(input_text))\n return metrics — Option D is correct because it uses the `@dsl.component` decorator with a `base_image` parameter, which is required for lightweight Python function components in Kubeflow Pipelines SDK v2. The decorator enables the component to run without a custom container by specifying a base image (here, `python:3.9`), and the function correctly returns a `Metrics` artifact after logging a metric. Without the decorator or with an incorrect decorator, the component would not be recognized as a pipeline component.
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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