PMLE Automating and Orchestrating ML Pipelines Practice Question
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?
⚠ Common exam trap
Candidates often 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.
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.
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 runs Apache Airflow DAGs, whose operators and sensors coordinate tasks across disparate GCP services with explicit dependencies and retry policies. This satisfies the stem's need to sequence a Vertex AI Pipeline, BigQuery job and Dataflow pipeline in one workflow.
- ✗
It automatically caches the outputs of each step to avoid recomputation.
Why it's wrong here
Airflow does not automatically cache task outputs; caching is something you implement yourself via XComs or external storage. Composer's actual advantage is orchestrating the Vertex AI Pipeline, BigQuery job and Dataflow pipeline as one dependency-managed DAG across services.
- ✗
It integrates natively with the Vertex AI Model Registry for model versioning.
Why it's wrong here
The Model Registry stores and versions models; it does not orchestrate a Vertex AI Pipeline, BigQuery job and Dataflow pipeline in sequence. Cloud Composer's advantage is cross-service DAG orchestration with dependencies, retries and scheduling, which is what this workflow needs.
- ✗
It provides a serverless execution environment for ML pipelines.
Why it's wrong here
Cloud Composer runs on GKE clusters you size and pay for, so it is not serverless; Vertex AI Pipelines provides the serverless ML pipeline execution. Composer's advantage here is orchestrating the Vertex AI Pipeline, BigQuery job and Dataflow pipeline together with dependencies and retries.
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