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 MLOps team wants to automate the retraining of a model each time new data arrives in a BigQuery table. What is the most efficient Google Cloud service to orchestrate this pipeline?
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 with a schedule trigger
Vertex AI Pipelines is purpose-built for orchestrating ML workflows, including model retraining. It integrates natively with BigQuery for data ingestion and supports schedule triggers to automate retraining upon new data arrival, making it the most efficient and managed option for this ML-specific task.
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 Composer with an Airflow DAG
Why it's wrong here
Cloud Composer is a managed Airflow environment, but for simpler ML retraining, Vertex AI Pipelines is more streamlined and purpose-built.
✗
Dataflow pipeline with a periodic trigger
Why it's wrong here
Dataflow is for data processing, not for orchestrating ML training and deployment steps.
✗
Cloud Functions triggered by BigQuery events
Why it's wrong here
Cloud Functions can trigger on BigQuery events, but managing pipeline steps becomes cumbersome without a dedicated orchestration service.
✓
Vertex AI Pipelines with a schedule trigger
Why this is correct
Vertex AI Pipelines natively supports scheduled triggers and is the recommended service for ML pipeline orchestration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse event-driven triggers with BigQuery's lack of native row-level or table-level event notifications, leading them to incorrectly choose Cloud Functions or Dataflow, while Vertex AI Pipelines provides the most integrated and efficient orchestration for ML retraining workflows.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines leverages Kubeflow Pipelines under the hood, allowing you to define retraining workflows as directed acyclic graphs (DAGs) with components for data extraction from BigQuery, model training, evaluation, and deployment. The schedule trigger uses Cloud Scheduler to invoke the pipeline at specified intervals or in response to new data, ensuring cost-efficient, serverless execution without managing infrastructure. In a real-world scenario, this setup can automatically retrain a model daily and push it to Vertex AI Model Registry only if performance metrics improve, avoiding unnecessary deployments.
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.
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: Vertex AI Pipelines with a schedule trigger — Vertex AI Pipelines is purpose-built for orchestrating ML workflows, including model retraining. It integrates natively with BigQuery for data ingestion and supports schedule triggers to automate retraining upon new data arrival, making it the most efficient and managed option for this ML-specific task.
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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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.
Question Discussion
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