- A
Install the library using pip in the pipeline definition
Why wrong: Pipeline definition is compiled and does not install packages; installation must be per-component.
- B
Use a container component with a pre-built image
Why wrong: This requires building a custom image, which the team wants to avoid.
- C
Use the packages_to_install parameter in @dsl.component
This parameter allows specifying extra packages to install in the component's execution environment.
- D
Add the library to the Vertex AI custom training image
Why wrong: This still requires building a custom image, which is not desired.
Question 951 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.
An ML pipeline runs on Vertex AI and includes a component that uses a third-party library not available in the default Python environment. The team wants to avoid building a custom container image. Which approach should they use?
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 packages_to_install parameter in @dsl.component
Option C is correct because the `packages_to_install` parameter in the `@dsl.component` decorator allows you to specify a list of third-party Python packages (e.g., via pip) that will be installed at runtime in the component's execution environment, without needing to build a custom container image. This is the recommended approach in Vertex AI Pipelines when you need to use a library not present in the default Python environment, as it avoids the overhead of custom container creation while ensuring the dependency is available for that specific 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.
- ✗
Install the library using pip in the pipeline definition
Why it's wrong here
Pipeline definition is compiled and does not install packages; installation must be per-component.
- ✗
Use a container component with a pre-built image
Why it's wrong here
This requires building a custom image, which the team wants to avoid.
- ✓
Use the packages_to_install parameter in @dsl.component
Why this is correct
This parameter allows specifying extra packages to install in the component's execution environment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add the library to the Vertex AI custom training image
Why it's wrong here
This still requires building a custom image, which is not desired.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the `packages_to_install` parameter with a generic pip install command in the pipeline definition, or they assume that using a pre-built container image does not count as building a custom container, when in fact any container image that includes the library must be custom-built or selected from a registry, which still requires image management overhead.
Detailed technical explanation
How to think about this question
Under the hood, the `packages_to_install` parameter in `@dsl.component` generates a pip install command that runs in the component's execution environment (a managed Vertex AI worker) before the component's Python code executes. This is implemented by appending the packages to a `requirements.txt` file that is installed via `pip install -r` in the component's startup script. A subtle behavior is that the installation happens each time the component runs, which can add latency; for frequently used libraries, pre-building a custom container might be more efficient, but for one-off or rarely used libraries, `packages_to_install` is the simplest approach.
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.
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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 packages_to_install parameter in @dsl.component — Option C is correct because the `packages_to_install` parameter in the `@dsl.component` decorator allows you to specify a list of third-party Python packages (e.g., via pip) that will be installed at runtime in the component's execution environment, without needing to build a custom container image. This is the recommended approach in Vertex AI Pipelines when you need to use a library not present in the default Python environment, as it avoids the overhead of custom container creation while ensuring the dependency is available for that specific 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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