- A
Deploy a custom Kubernetes cron job on GKE to run the training script directly.
Why wrong: This adds cluster management overhead.
- B
Use Cloud Composer (Airflow) to schedule the pipeline with a DAG.
Why wrong: Overkill for a simple daily schedule; adds complexity.
- C
Use Cloud Scheduler to publish a Pub/Sub message daily, which triggers a Cloud Function that starts the Vertex AI Pipeline.
This provides automated daily triggering with minimal overhead.
- D
Use Dataflow to continuously read from BigQuery and trigger training when new data arrives.
Why wrong: Dataflow is for streaming, but the requirement is daily batch.
- E
Use Vertex AI Pipelines to define the workflow and preemptible VMs for training to reduce cost.
Preemptible VMs are cost-effective and Vertex AI Pipelines orchestrates the workflow.
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 team is designing an automated pipeline to retrain a recommendation model every day using new user interaction data stored in BigQuery. The pipeline must be cost-efficient, scalable, and require minimal manual intervention. Which two approaches should they consider?
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 Cloud Scheduler to publish a Pub/Sub message daily, which triggers a Cloud Function that starts the Vertex AI Pipeline.
Option C is correct because Cloud Scheduler triggers a Pub/Sub message that invokes a Cloud Function, which starts a Vertex AI Pipeline. This serverless approach is cost-efficient (no idle compute), scales automatically, and requires minimal manual intervention. Option E is correct because Vertex AI Pipelines natively orchestrates ML workflows, and using preemptible VMs reduces training costs by up to 80% while maintaining scalability.
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.
- ✗
Deploy a custom Kubernetes cron job on GKE to run the training script directly.
Why it's wrong here
This adds cluster management overhead.
- ✗
Use Cloud Composer (Airflow) to schedule the pipeline with a DAG.
Why it's wrong here
Overkill for a simple daily schedule; adds complexity.
- ✓
Use Cloud Scheduler to publish a Pub/Sub message daily, which triggers a Cloud Function that starts the Vertex AI Pipeline.
Why this is correct
This provides automated daily triggering with minimal overhead.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Dataflow to continuously read from BigQuery and trigger training when new data arrives.
Why it's wrong here
Dataflow is for streaming, but the requirement is daily batch.
- ✓
Use Vertex AI Pipelines to define the workflow and preemptible VMs for training to reduce cost.
Why this is correct
Preemptible VMs are cost-effective and Vertex AI Pipelines orchestrates the workflow.
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 batch scheduling (Cloud Scheduler) and continuous streaming (Dataflow), and candidates mistakenly choose Dataflow because they think 'new data' implies real-time, but the requirement is a daily retrain, not a streaming trigger.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines uses Kubeflow Pipelines or TFX under the hood to define a DAG of steps, and preemptible VMs are Compute Engine instances that last up to 24 hours but can be terminated earlier; they are ideal for fault-tolerant training jobs that can checkpoint and resume. Cloud Scheduler with Pub/Sub and Cloud Functions follows an event-driven architecture where the function authenticates via a service account and calls the Vertex AI Pipeline API (projects.locations.pipelineJobs.create) to start the run, ensuring no persistent infrastructure is needed.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 Cloud Scheduler to publish a Pub/Sub message daily, which triggers a Cloud Function that starts the Vertex AI Pipeline. — Option C is correct because Cloud Scheduler triggers a Pub/Sub message that invokes a Cloud Function, which starts a Vertex AI Pipeline. This serverless approach is cost-efficient (no idle compute), scales automatically, and requires minimal manual intervention. Option E is correct because Vertex AI Pipelines natively orchestrates ML workflows, and using preemptible VMs reduces training costs by up to 80% while maintaining scalability.
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: Jun 30, 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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