easyMultiple Select
PMLE Practice Question: Which TWO are benefits of using Vertex AI…
Which TWO are benefits of using Vertex AI Pipelines for ML workflow orchestration over deploying custom Airflow DAGs in Cloud Composer? (Choose TWO.)
⚠ Common exam trap
Google Cloud often tests the misconception that managed infrastructure and scheduling are unique to Vertex AI Pipelines, when in fact Cloud Composer also provides these features, so candidates must focus on the specific differentiators like native integration and automatic lineage tracking.
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
✓
Automatic artifact lineage tracking
Option C is correct because Vertex AI Pipelines automatically tracks artifact lineage through ML Metadata, recording inputs, outputs, and parameters of each pipeline step without requiring custom instrumentation. Option D is correct because Vertex AI Pipelines natively integrates with Vertex AI services such as Vertex AI Training, Vertex AI Endpoints, and the Model Registry, enabling seamless handoff between pipeline steps and managed ML resources. In contrast, option A is not the distinguishing benefit here since Cloud Composer is also a managed service that abstracts much of the underlying infrastructure. Option B is incorrect because Cloud Composer (Airflow) also provides robust built-in scheduling capabilities. Option E is incorrect because both Airflow DAGs and Vertex AI Pipelines support arbitrary Python code in their steps, so this is not a unique advantage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Managed infrastructure without manual configuration
Why it's wrong here
Cloud Composer is itself a managed Airflow service, so Google handles cluster provisioning, scaling and patching. Managed infrastructure therefore does not distinguish Vertex AI Pipelines from Composer; both remove manual configuration of the underlying environment.
- ✗
Built-in scheduling capabilities
Why it's wrong here
Cloud Composer's Airflow scheduler already provides cron-based scheduling, so scheduling is not a differentiator. Vertex AI Pipelines offers managed scheduling too, but the question asks for advantages over Composer, and this capability exists in both.
- ✓
Automatic artifact lineage tracking
Why this is correct
Vertex AI Pipelines records each step's inputs and outputs as ML Metadata artifacts, automatically building lineage across the run. This satisfies the stem's orchestration benefit: unlike custom Airflow DAGs in Cloud Composer, where lineage must be coded manually, the managed service captures provenance without extra engineering effort.
- ✓
Native integration with Vertex AI services
Why this is correct
Vertex AI Pipelines natively integrates with Vertex AI services such as Training, Endpoints and Model Registry, removing custom operator code that Airflow DAGs need. This native integration satisfies the benefit of streamlined orchestration across the Vertex AI platform.
- ✗
Support for arbitrary Python code in steps
Why it's wrong here
Arbitrary Python execution is not unique to Vertex AI Pipelines; Airflow's PythonOperator runs any Python callable, so this fails to differentiate the two. It tempts because custom code flexibility matters when orchestrating bespoke preprocessing, yet that need is already met by Cloud Composer, making it no benefit over Airflow DAGs.
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