- A
Cloud Composer
Airflow can orchestrate Vertex AI pipelines, BigQuery jobs, and Dataflow pipelines with dependencies.
- B
Vertex AI Pipelines
Vertex AI Pipelines can also orchestrate ML steps and call BigQuery and Dataflow via custom components.
- C
Cloud Scheduler
Why wrong: Only for time-based scheduling, not orchestration.
- D
BigQuery scheduled queries
Why wrong: Only for BigQuery queries, not full orchestration.
- E
Cloud Functions
Why wrong: Not designed for complex orchestration with dependencies and retries.
Question 465 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.
You need to orchestrate a complex ML workflow that involves multiple Vertex AI pipelines, BigQuery jobs, and Dataflow pipelines. The workflow must handle dependencies, retries, and monitoring. Which two services are best suited for this orchestration?
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
✓
Cloud Composer
Cloud Composer (based on Apache Airflow) is the correct choice because it provides a managed environment for orchestrating complex workflows with dependencies, retries, and monitoring across heterogeneous services like Vertex AI pipelines, BigQuery, and Dataflow. Airflow's DAGs allow you to define task dependencies, set retry policies, and integrate with Cloud Monitoring for observability, making it ideal for multi-service ML workflows.
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
Why this is correct
Airflow can orchestrate Vertex AI pipelines, BigQuery jobs, and Dataflow pipelines with dependencies.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines can also orchestrate ML steps and call BigQuery and Dataflow via custom components.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Scheduler
Why it's wrong here
Only for time-based scheduling, not orchestration.
- ✗
BigQuery scheduled queries
Why it's wrong here
Only for BigQuery queries, not full orchestration.
- ✗
Cloud Functions
Why it's wrong here
Not designed for complex orchestration with dependencies and retries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Cloud Scheduler or Cloud Functions as sufficient for orchestration, but they lack the dependency management, retry logic, and cross-service monitoring that Cloud Composer provides for complex ML workflows.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Composer uses Apache Airflow's DAG scheduler, which manages task dependencies via a directed acyclic graph and supports retries through exponential backoff (configurable via `retry_delay` and `max_retry_delay`). For monitoring, Airflow integrates with Cloud Logging and Cloud Monitoring, allowing you to set alerts on task failures or SLA misses. In a real-world scenario, you might use Airflow's `BigQueryOperator`, `DataflowCreatePythonJobOperator`, and `VertexAIPipelineOperator` to chain jobs, with `depends_on_past` to enforce sequential execution and `trigger_rule` for conditional branching.
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: Cloud Composer — Cloud Composer (based on Apache Airflow) is the correct choice because it provides a managed environment for orchestrating complex workflows with dependencies, retries, and monitoring across heterogeneous services like Vertex AI pipelines, BigQuery, and Dataflow. Airflow's DAGs allow you to define task dependencies, set retry policies, and integrate with Cloud Monitoring for observability, making it ideal for multi-service ML workflows.
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 →
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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