- A
DataflowStartFlexTemplateJobOperator
Why wrong: This operator launches a Dataflow Flex Template job, not a Vertex AI pipeline.
- B
VertexAICreateCustomJobOperator
Why wrong: This operator is for creating a custom training job, not a pipeline run.
- C
MLEngineTrainingOperator
Why wrong: This is a legacy operator for AI Platform Training, not Vertex AI Pipelines.
- D
VertexAIPipelineJobOperator
This operator triggers a Vertex AI Pipeline job within an Airflow DAG.
Question 446 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.
A data science team uses Cloud Composer to orchestrate ML workflows. They need to trigger a Vertex AI pipeline after a BigQuery data load completes, and then run a Dataflow job. Which Airflow operator should they use to launch the Vertex AI pipeline?
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
✓
VertexAIPipelineJobOperator
Option D is correct because the VertexAIPipelineJobOperator is specifically designed to trigger a Vertex AI pipeline run from within an Airflow DAG. This operator directly corresponds to the requirement of launching a Vertex AI pipeline after a BigQuery data load completes, making it the appropriate choice for orchestrating ML workflows with Cloud Composer.
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.
- ✗
DataflowStartFlexTemplateJobOperator
Why it's wrong here
This operator launches a Dataflow Flex Template job, not a Vertex AI pipeline.
- ✗
VertexAICreateCustomJobOperator
Why it's wrong here
This operator is for creating a custom training job, not a pipeline run.
- ✗
MLEngineTrainingOperator
Why it's wrong here
This is a legacy operator for AI Platform Training, not Vertex AI Pipelines.
- ✓
VertexAIPipelineJobOperator
Why this is correct
This operator triggers a Vertex AI Pipeline job within an Airflow DAG.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between operators for different Vertex AI services (e.g., custom jobs vs. pipelines), so candidates may confuse VertexAICreateCustomJobOperator with VertexAIPipelineJobOperator when the question specifically asks for launching a pipeline.
Detailed technical explanation
How to think about this question
The VertexAIPipelineJobOperator submits a pipeline job to Vertex AI Pipelines, which is a managed service for building, deploying, and monitoring ML pipelines. Under the hood, it uses the Vertex AI SDK to create a PipelineJob resource, which can be parameterized with runtime arguments and configured to use a specific service account. In real-world scenarios, this operator is often combined with BigQuery operators in a DAG to ensure data is loaded before the pipeline starts, and then followed by a Dataflow operator for post-processing.
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.
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Automating and Orchestrating ML Pipelines — study guide chapter
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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: VertexAIPipelineJobOperator — Option D is correct because the VertexAIPipelineJobOperator is specifically designed to trigger a Vertex AI pipeline run from within an Airflow DAG. This operator directly corresponds to the requirement of launching a Vertex AI pipeline after a BigQuery data load completes, making it the appropriate choice for orchestrating ML workflows with Cloud Composer.
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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