The answer is to define pipelines using the Kubeflow Pipelines SDK. This is correct because Vertex AI Pipelines natively executes container-based components, and the Kubeflow Pipelines SDK provides the standard, declarative way to orchestrate these Docker containers as a directed acyclic graph of steps, ensuring each component is fully isolated, reproducible, and scalable. On the Google Professional Machine Learning Engineer exam, this tests your understanding of how Vertex AI Pipelines extends open-source Kubeflow, and a common trap is confusing it with other orchestration tools like Cloud Composer or assuming you must write raw YAML. The core concept is that the SDK handles the complex orchestration logic, dependency injection, and artifact tracking automatically. For a memory tip, remember that Vertex AI Pipelines is essentially managed Kubeflow, so if you think “Kubeflow SDK,” you are thinking the right way to define your pipeline steps.
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. 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.
Which TWO are best practices for building ML pipelines on Vertex AI Pipelines?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a container-based approach for each component
Option C is correct because Vertex AI Pipelines is designed to run container-based components, where each step in the pipeline is a Docker container that encapsulates its dependencies and execution logic. This approach ensures reproducibility, isolation, and scalability, aligning with best practices for ML pipelines on Vertex AI.
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.
✗
Store all trained models in Cloud Storage without versioning
Why it's wrong here
Should use Vertex AI Model Registry for versioning.
✗
Use Cloud Build as the pipeline orchestrator
Why it's wrong here
Cloud Build is for CI/CD, not ML pipeline orchestration.
✓
Use a container-based approach for each component
Why this is correct
Containerized components are reusable and scalable.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Define pipelines using the Kubeflow Pipelines SDK
Why this is correct
Vertex AI Pipelines is based on Kubeflow Pipelines.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Cloud Composer as the primary pipeline tool
Why it's wrong here
Cloud Composer is for workflow orchestration but not the best practice for Vertex AI Pipelines.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between general-purpose orchestration tools (Cloud Composer, Cloud Build) and ML-specific pipeline services (Vertex AI Pipelines), expecting candidates to recognize that container-based components and the Kubeflow Pipelines SDK are the correct building blocks for ML pipelines on Vertex AI.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines leverages the Kubeflow Pipelines SDK (option D) to define pipelines as directed acyclic graphs (DAGs) of container-based components. Under the hood, each component is a Docker image that runs on the Vertex AI managed infrastructure, with inputs and outputs passed as Cloud Storage URIs or artifacts. This design allows for automatic caching of component outputs, parallel execution of independent steps, and seamless integration with Vertex AI services like Model Registry and Hyperparameter Tuning.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a container-based approach for each component — Option C is correct because Vertex AI Pipelines is designed to run container-based components, where each step in the pipeline is a Docker container that encapsulates its dependencies and execution logic. This approach ensures reproducibility, isolation, and scalability, aligning with best practices for ML pipelines on Vertex AI.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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