- A
It allows orchestrating heterogeneous workflows across multiple GCP services with dependencies and retries.
Cloud Composer excels at orchestrating complex DAGs that span multiple services like Vertex AI, BigQuery, and Dataflow.
- B
It automatically caches the outputs of each step to avoid recomputation.
Why wrong: Caching is a feature of Vertex AI Pipelines, not Cloud Composer.
- C
It integrates natively with the Vertex AI Model Registry for model versioning.
Why wrong: Cloud Composer can interact with Model Registry via API, but it is not a native integration feature.
- D
It provides a serverless execution environment for ML pipelines.
Why wrong: Cloud Composer is not serverless; it runs on GKE. Vertex AI Pipelines itself is serverless for ML pipelines.
Question 485 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 organization wants to use Cloud Composer (Airflow) to orchestrate a machine learning workflow that includes running a Vertex AI Pipeline, followed by a BigQuery job, and then a Dataflow pipeline. What is the primary advantage of using Cloud Composer for this orchestration?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"primary"Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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
✓
It allows orchestrating heterogeneous workflows across multiple GCP services with dependencies and retries.
Cloud Composer (Apache Airflow) is designed to orchestrate heterogeneous workflows across multiple GCP services. In this scenario, it can define a Directed Acyclic Graph (DAG) that runs a Vertex AI Pipeline, then a BigQuery job, and finally a Dataflow pipeline, with built-in support for dependency management, retries, and failure handling. This is the primary advantage because it allows you to coordinate disparate services in a single, reliable workflow.
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.
- ✓
It allows orchestrating heterogeneous workflows across multiple GCP services with dependencies and retries.
Why this is correct
Cloud Composer excels at orchestrating complex DAGs that span multiple services like Vertex AI, BigQuery, and Dataflow.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
It automatically caches the outputs of each step to avoid recomputation.
Why it's wrong here
Caching is a feature of Vertex AI Pipelines, not Cloud Composer.
- ✗
It integrates natively with the Vertex AI Model Registry for model versioning.
Why it's wrong here
Cloud Composer can interact with Model Registry via API, but it is not a native integration feature.
- ✗
It provides a serverless execution environment for ML pipelines.
Why it's wrong here
Cloud Composer is not serverless; it runs on GKE. Vertex AI Pipelines itself is serverless for ML pipelines.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Cloud Composer's orchestration capabilities with features specific to individual GCP services (like caching, model registry, or serverless execution), leading them to pick options that describe those services' features rather than the primary advantage of using an orchestrator.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Composer uses Airflow's DAG scheduler and executor to manage task dependencies, retries, and state persistence via a metadata database (Cloud SQL). For example, if the BigQuery job fails, Airflow can automatically retry it based on configured retry policies, and downstream tasks (like the Dataflow pipeline) will not execute until the upstream task succeeds. In a real-world scenario, this is critical for production ML pipelines where data drift or transient errors require robust orchestration without manual intervention.
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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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: It allows orchestrating heterogeneous workflows across multiple GCP services with dependencies and retries. — Cloud Composer (Apache Airflow) is designed to orchestrate heterogeneous workflows across multiple GCP services. In this scenario, it can define a Directed Acyclic Graph (DAG) that runs a Vertex AI Pipeline, then a BigQuery job, and finally a Dataflow pipeline, with built-in support for dependency management, retries, and failure handling. This is the primary advantage because it allows you to coordinate disparate services in a single, reliable workflow.
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.
Are there clue words in this question I should notice?
Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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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