- A
Use BigQuery scheduled queries to run the training script on a schedule.
Why wrong: BigQuery scheduled queries are for SQL queries, not running ML training jobs.
- B
Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.
Vertex AI Pipelines is purpose-built for ML pipelines.
- C
Use AI Platform Notebooks to schedule the training job on a recurring basis.
Why wrong: Notebooks are for interactive development, not scheduling production pipelines.
- D
Use Cloud Build and Cloud Functions to trigger the pipeline when new training data arrives in Cloud Storage.
Event-driven triggers automate pipeline execution on data arrival.
- E
Use Cloud Composer to orchestrate the pipeline steps, including data extraction, preprocessing, training, and deployment.
Cloud Composer (Airflow) is designed for orchestrating complex workflows with dependencies.
Quick Answer
The answer is Vertex AI Pipelines, as it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs), which directly supports automated weekly retraining with minimal manual intervention. This design enables you to define reusable components for data extraction, preprocessing, training, and deployment, and schedule the pipeline runs natively with Cloud Scheduler or event triggers, integrating seamlessly with TensorFlow and Google Cloud services. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish between managed orchestration services and custom solutions—a common trap is choosing Cloud Composer for pipeline orchestration, but Vertex AI Pipelines is the correct choice for ML-specific workflows because it is purpose-built for ML DAGs and reduces operational overhead. Remember the memory tip: for ML pipelines, think "Vertex AI Pipelines for the DAG, Cloud Composer for the DAG's dependencies."
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.
You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)
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 Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.
Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.
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.
- ✗
Use BigQuery scheduled queries to run the training script on a schedule.
Why it's wrong here
BigQuery scheduled queries are for SQL queries, not running ML training jobs.
- ✓
Use Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components.
Why this is correct
Vertex AI Pipelines is purpose-built for ML pipelines.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AI Platform Notebooks to schedule the training job on a recurring basis.
Why it's wrong here
Notebooks are for interactive development, not scheduling production pipelines.
- ✓
Use Cloud Build and Cloud Functions to trigger the pipeline when new training data arrives in Cloud Storage.
Why this is correct
Event-driven triggers automate pipeline execution on data arrival.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Cloud Composer to orchestrate the pipeline steps, including data extraction, preprocessing, training, and deployment.
Why this is correct
Cloud Composer (Airflow) is designed for orchestrating complex workflows with dependencies.
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 confusing development tools (like Notebooks) or data-query services (like BigQuery scheduled queries) with production-grade orchestration services, leading candidates to select options that cannot handle multi-step pipeline dependencies or automated scheduling in a managed, scalable way.
Detailed technical explanation
How to think about this question
Vertex AI Pipelines uses the Kubeflow Pipelines SDK or TFX to define components that run as containerized steps, with automatic artifact lineage tracking and caching. Under the hood, each pipeline run is executed on a managed Kubernetes cluster, and you can trigger runs via Cloud Scheduler using Pub/Sub or by listening to Cloud Storage events with Cloud Functions, enabling event-driven retraining when new data arrives. This design ensures idempotency and reproducibility, critical for large-scale recommendation systems where data drift or model decay must be addressed without manual oversight.
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
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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 Vertex AI Pipelines to define the ML pipeline as a Directed Acyclic Graph (DAG) of components. — Vertex AI Pipelines (option B) is correct because it provides a managed, serverless orchestration service for building, testing, and deploying ML pipelines as Directed Acyclic Graphs (DAGs). This directly supports the requirement for automated, minimal-intervention weekly retraining by allowing you to define reusable components and schedule pipeline runs via Cloud Scheduler or event triggers, integrating natively with TensorFlow and Google Cloud services.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 11, 2026
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