The answer is Vertex AI Pipelines and Cloud Scheduler. Vertex AI Pipelines is the correct orchestration service because it allows you to define a directed acyclic graph (DAG) of steps—such as data preprocessing, training, evaluation, and deployment—as a reusable, serverless workflow, while Cloud Scheduler provides the time-based triggers to automatically launch these pipelines at regular intervals, forming a complete automated retraining pipeline on Vertex AI. On the Google Professional Machine Learning Engineer exam, this pairing tests your understanding of how to decouple scheduling from pipeline logic; a common trap is choosing Cloud Functions or Pub/Sub for scheduling, but Cloud Scheduler is the native, cost-effective choice for cron-based triggers. Remember the mnemonic “Schedule the Pipeline” to link Cloud Scheduler with Vertex AI Pipelines for automated retraining.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. 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 services are commonly used together to implement an end-to-end ML pipeline that automatically retrains and deploys models on Vertex AI? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vertex AI Pipelines
Vertex AI Pipelines (B) is the correct choice because it provides a serverless, scalable orchestration service specifically designed to build, run, and manage ML pipelines on Vertex AI. It enables you to define a directed acyclic graph (DAG) of steps—including data preprocessing, training, evaluation, and deployment—and can be triggered automatically to retrain and deploy models. Cloud Scheduler (E) is commonly used together with Vertex AI Pipelines to schedule pipeline runs at fixed intervals or in response to time-based triggers, forming a complete end-to-end automated retraining and deployment workflow.
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.
✗
Cloud Dataflow
Why it's wrong here
Dataflow is for data processing, not directly needed in retraining pipeline if using Vertex AI training.
✓
Vertex AI Pipelines
Why this is correct
Pipelines orchestrate the training and deployment steps.
Related concept
Read the scenario before looking for a memorised answer.
✗
Cloud Composer
Why it's wrong here
Composer can orchestrate but is more complex; Vertex AI Pipelines is simpler.
✗
Cloud Source Repositories
Why it's wrong here
Used for code versioning, not pipeline orchestration.
✓
Cloud Scheduler
Why this is correct
Scheduler triggers pipeline runs on schedule.
Related concept
Read the scenario before looking for a memorised answer.
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) and ML-native pipeline services (Vertex AI Pipelines), leading candidates to pick Cloud Composer because of its familiarity with Airflow, even though Vertex AI Pipelines is the correct, integrated choice for end-to-end ML workflows on Vertex AI.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines uses the Kubeflow Pipelines SDK or the TFX SDK to define pipeline components and compile them into a pipeline specification. Under the hood, each pipeline run is executed as a series of containerized steps on Vertex AI's managed infrastructure, with automatic artifact tracking and lineage. Cloud Scheduler triggers these pipelines via Pub/Sub or HTTP requests to the Vertex AI API, enabling time-based retraining (e.g., daily or weekly) without manual intervention, which is critical for production ML systems that must adapt to data drift.
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.
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Pipelines — Vertex AI Pipelines (B) is the correct choice because it provides a serverless, scalable orchestration service specifically designed to build, run, and manage ML pipelines on Vertex AI. It enables you to define a directed acyclic graph (DAG) of steps—including data preprocessing, training, evaluation, and deployment—and can be triggered automatically to retrain and deploy models. Cloud Scheduler (E) is commonly used together with Vertex AI Pipelines to schedule pipeline runs at fixed intervals or in response to time-based triggers, forming a complete end-to-end automated retraining and deployment workflow.
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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Question Discussion
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