PMLE Practice Question: Responsible for maintaining an ML pipeline that…

You are responsible for maintaining an ML pipeline that runs daily on Vertex AI Pipelines. The pipeline preprocesses data, trains a model, and deploys it to an endpoint. Recently, the pipeline has been failing at the deployment step because the endpoint already exists and the deploy step tries to create a new endpoint instead of updating the existing one. The pipeline code is written using the Kubeflow Pipelines SDK. You need to modify the pipeline to resolve this issue with minimal changes. What should you do?

⚠ Common exam trap

PMLE often tests idempotency in pipeline components. Candidates might choose retries or manual deletion, but the correct answer is to implement conditional logic (create vs. update) within the component, as it's the minimal code change that addresses the root cause.

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

✓

In the deployment component, add a check to verify if the endpoint exists, and if so, call the update endpoint method instead of create.

The pipeline fails because the deployment component attempts to create a new endpoint when one already exists. The minimal change is to modify the deployment component to check for the endpoint's existence and, if it exists, call the update method instead of create. This ensures idempotency and resolves the failure without altering the pipeline structure or adding external dependencies.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Change the pipeline to use a Cloud Function that triggers the deployment independently, bypassing Vertex AI Pipelines.
Why it's wrong here
A Cloud Function bypasses Vertex AI Pipelines entirely, breaking pipeline orchestration, lineage and the minimal-change requirement. It is tempting because serverless triggers decouple deployment, and that pattern suits event-driven deployments, but here the existing deploy component should be amended to update the endpoint.
✓
In the deployment component, add a check to verify if the endpoint exists, and if so, call the update endpoint method instead of create.
Why this is correct
Checking whether the endpoint already exists and calling the update method instead of create resolves the failure directly within the deployment component, requiring minimal changes to the existing Kubeflow Pipelines SDK code while preserving the daily pipeline structure.
✗
Set the deploy component's retry policy to infinite so it eventually succeeds.
Why it's wrong here
Retries cannot fix a deterministic conflict: the deploy component calls endpoint creation, which fails every time because the endpoint name already exists. Retry policies suit transient faults such as timeouts or throttling, not logic errors. The fix requires changing the component to call update on the existing endpoint instead.
✗
Manually delete the existing endpoint before each pipeline run.
Why it's wrong here
Deleting the endpoint manually each run is an out-of-band operational step, not a pipeline modification, so the daily run still fails unattended and downtime is introduced. Manual intervention suits one-off migrations, whereas the requirement is a code change that updates the existing endpoint in place.

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PMLE Practice Question: Responsible for maintaining an ML pipeline that…

Why each option matters

Option-by-option breakdown

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