- A
BigQueryOperator to run the pipeline as a query.
Why wrong: BigQueryOperator is for SQL queries, not pipeline execution.
- B
VertexAIPipelineRunOperator or the Google Cloud Pipeline operator.
These operators are designed to run Vertex AI pipelines from Airflow.
- C
PythonOperator with a custom script using the google-cloud-aiplatform library.
Why wrong: While possible, it's not the standard operator and requires manual error handling.
- D
VertexAIPipelineRunOperator (or Airflow's GCSToGCSOperator) for pipeline orchestration.
Why wrong: GCSToGCSOperator is for file transfer, not pipeline runs. The correct operator is VertexAIPipelineRunOperator.
Question 248 of 1,000
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 engineer is using Cloud Composer (Airflow) to orchestrate a ML workflow. They need to run a Vertex AI pipeline as one of the tasks in the DAG. Which Airflow operator should they use?
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
✓
VertexAIPipelineRunOperator or the Google Cloud Pipeline operator.
Option B is correct because Cloud Composer (Airflow) natively supports the `VertexAIPipelineRunOperator` (or its alias `GoogleCloudPipelineOperator`), which is specifically designed to trigger and monitor a Vertex AI pipeline run as a task within a DAG. This operator handles authentication, pipeline job submission, and status polling without requiring custom code, making it the idiomatic choice for orchestrating Vertex AI pipelines from Airflow.
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.
- ✗
BigQueryOperator to run the pipeline as a query.
Why it's wrong here
BigQueryOperator is for SQL queries, not pipeline execution.
- ✓
VertexAIPipelineRunOperator or the Google Cloud Pipeline operator.
Why this is correct
These operators are designed to run Vertex AI pipelines from Airflow.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
PythonOperator with a custom script using the google-cloud-aiplatform library.
Why it's wrong here
While possible, it's not the standard operator and requires manual error handling.
- ✗
VertexAIPipelineRunOperator (or Airflow's GCSToGCSOperator) for pipeline orchestration.
Why it's wrong here
GCSToGCSOperator is for file transfer, not pipeline runs. The correct operator is VertexAIPipelineRunOperator.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse a general-purpose operator (like PythonOperator or GCSToGCSOperator) with a purpose-built operator, or incorrectly assume that BigQueryOperator can be repurposed for pipeline execution, when the exam expects knowledge of the specific Airflow operator designed for Vertex AI pipeline orchestration.
Detailed technical explanation
How to think about this question
The `VertexAIPipelineRunOperator` internally uses the `google-cloud-aiplatform` Python client to call the `PipelineJob.submit()` method and then polls the job status via the `PipelineJob.state` field until it reaches a terminal state (e.g., `PIPELINE_STATE_SUCCEEDED` or `PIPELINE_STATE_FAILED`). A subtle behavior is that the operator supports both synchronous and asynchronous execution modes via the `wait_until_finished` parameter; in asynchronous mode, the DAG task completes immediately after submission, and downstream tasks must rely on external sensors or separate monitoring. In real-world ML workflows, this operator is critical for chaining data preprocessing, training, and deployment pipelines while leveraging Vertex AI's managed infrastructure and artifact tracking.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
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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: VertexAIPipelineRunOperator or the Google Cloud Pipeline operator. — Option B is correct because Cloud Composer (Airflow) natively supports the `VertexAIPipelineRunOperator` (or its alias `GoogleCloudPipelineOperator`), which is specifically designed to trigger and monitor a Vertex AI pipeline run as a task within a DAG. This operator handles authentication, pipeline job submission, and status polling without requiring custom code, making it the idiomatic choice for orchestrating Vertex AI pipelines from Airflow.
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
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Last reviewed: Jul 4, 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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