- A
Use ContainerOp class from kfp.v2.dsl.
Why wrong: ContainerOp is from KFP SDK v1, not v2.
- B
Use the @dsl.container_component decorator and set the image parameter to the URI.
This is the correct way to define a container component and specify the image URI.
- C
Use a placeholder in the pipeline YAML.
Why wrong: Not a standard approach; the image must be specified in the component definition.
- D
Use the @dsl.component decorator and set the base_image parameter.
Why wrong: @dsl.component is for Python function-based components, not container components.
Question 902 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.
You are designing a Vertex AI pipeline that includes a container component. The component needs to use a custom container image that is stored in Artifact Registry. How should you specify the container image in the component definition?
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 the @dsl.container_component decorator and set the image parameter to the URI.
Option B is correct because the `@dsl.container_component` decorator is specifically designed for defining container components in Vertex AI pipelines. The `image` parameter accepts the full Artifact Registry URI (e.g., `us-central1-docker.pkg.dev/my-project/my-repo/my-image:tag`), allowing the pipeline to pull the custom container from Artifact Registry at runtime. This decorator also requires you to define inputs and outputs explicitly, ensuring the component is properly integrated into the pipeline graph.
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 ContainerOp class from kfp.v2.dsl.
Why it's wrong here
ContainerOp is from KFP SDK v1, not v2.
- ✓
Use the @dsl.container_component decorator and set the image parameter to the URI.
Why this is correct
This is the correct way to define a container component and specify the image URI.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a placeholder in the pipeline YAML.
Why it's wrong here
Not a standard approach; the image must be specified in the component definition.
- ✗
Use the @dsl.component decorator and set the base_image parameter.
Why it's wrong here
@dsl.component is for Python function-based components, not container components.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the `@dsl.component` decorator (for Python functions) with the `@dsl.container_component` decorator (for custom containers), mistakenly thinking `base_image` can point to a custom container image in Artifact Registry.
Detailed technical explanation
How to think about this question
Under the hood, the `@dsl.container_component` decorator generates a component specification that includes the `container` field in the pipeline JSON/YAML, where the `image` URI is resolved at compile time. A subtle behavior is that the image URI must be a fully qualified Artifact Registry path (including region, project, repository, and tag/digest) to avoid runtime pull failures. In real-world scenarios, using a digest (e.g., `@sha256:...`) instead of a tag ensures immutability and prevents unexpected updates when the tag is overwritten.
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 the @dsl.container_component decorator and set the image parameter to the URI. — Option B is correct because the `@dsl.container_component` decorator is specifically designed for defining container components in Vertex AI pipelines. The `image` parameter accepts the full Artifact Registry URI (e.g., `us-central1-docker.pkg.dev/my-project/my-repo/my-image:tag`), allowing the pipeline to pull the custom container from Artifact Registry at runtime. This decorator also requires you to define inputs and outputs explicitly, ensuring the component is properly integrated into the pipeline graph.
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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