- A
Use a pre-built Google Cloud Pipeline Component for Vertex AI Training with custom container configuration.
Why wrong: Pre-built components may not support arbitrary custom scripts as easily as a custom container component.
- B
Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs.
ContainerComponent allows defining a custom container component with explicit inputs and outputs.
- C
Define a Python function component with @dsl.component and include the container code inline.
Why wrong: Python function components run in a default Python image, not a custom container.
- D
Use the importer component to import the script and then run it as a task.
Why wrong: Importer is for importing artifacts, not for running custom containers.
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.
An ML engineer wants to containerize a custom training script and use it as a component in a Vertex AI Pipeline. The component should accept a dataset URI and a learning rate parameter, and output a trained model artifact. Which approach should the engineer use to define the 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
Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs.
Option B is correct because ContainerComponent from kfp.v2.components allows you to define a custom container component by specifying the container image, command, inputs, and outputs directly. This is the appropriate approach when you have a custom training script that you want to containerize and use as a component in a Vertex AI Pipeline, as it gives you full control over the container configuration and artifact handling.
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.
- ✗
Use a pre-built Google Cloud Pipeline Component for Vertex AI Training with custom container configuration.
Why it's wrong here
Pre-built components may not support arbitrary custom scripts as easily as a custom container component.
- ✓
Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs.
Why this is correct
ContainerComponent allows defining a custom container component with explicit inputs and outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Define a Python function component with @dsl.component and include the container code inline.
Why it's wrong here
Python function components run in a default Python image, not a custom container.
- ✗
Use the importer component to import the script and then run it as a task.
Why it's wrong here
Importer is for importing artifacts, not for running custom containers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between Python function components and container components, and the trap here is that candidates may confuse @dsl.component (for Python functions) with containerized components, leading them to choose Option C even though it cannot properly define a container image and artifact outputs.
Detailed technical explanation
How to think about this question
ContainerComponent works by generating a component definition that includes a container image URI, command, and arguments, and it uses the KFP artifact system to pass inputs and outputs as typed artifacts (e.g., Dataset, Model). Under the hood, it creates a component YAML that the Vertex AI Pipelines service interprets to launch a container on the AI Platform, handling serialization and deserialization of artifacts via metadata stores. In real-world scenarios, this is critical when you need to use a custom Docker image with specific dependencies (e.g., TensorFlow, PyTorch) and want to ensure that the trained model artifact is correctly registered in Vertex AI's metadata store for lineage tracking.
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: Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs. — Option B is correct because ContainerComponent from kfp.v2.components allows you to define a custom container component by specifying the container image, command, inputs, and outputs directly. This is the appropriate approach when you have a custom training script that you want to containerize and use as a component in a Vertex AI Pipeline, as it gives you full control over the container configuration and artifact handling.
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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