PMLE Automating and Orchestrating ML Pipelines Practice Question

An ML engineer has a Vertex AI pipeline that trains a model and then evaluates it. The engineer wants the pipeline to automatically deploy the model to an endpoint only if the evaluation metric (e.g., accuracy) exceeds a threshold. The pipeline is defined using the Kubeflow Pipelines SDK. Which approach should the engineer use to implement this conditional deployment?

⚠ Common exam trap

The trap here is assuming that evaluation components or deployment components have built-in conditional flags, when actually the condition must be explicitly defined in the pipeline graph.

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

✓

Use a Vertex AI Pipelines condition with a comparison of the metric output to the threshold, and place the deployment component inside the condition's 'then' branch.

Conditional deployment in Vertex AI Pipelines is achieved by using dsl.Condition to evaluate a metric against a threshold. The deployment component is placed inside the condition's true branch, ensuring it only executes when the metric meets the requirement. This provides a clean, orchestrated way to gate deployment without external services or manual intervention.

Answer analysis

Option-by-option breakdown

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

✗
Set the deployment component's 'condition' parameter to the metric value, and rely on the component to skip itself if the value is below threshold.
Why it's wrong here
There is no built-in 'condition' parameter in a Vertex AI Pipelines component that automatically skips execution based on a metric. Conditional execution is handled at the pipeline orchestration level using dsl.Condition, not within the component itself.
✗
Configure the pipeline to always deploy, and use a Cloud Function triggered by a Pub/Sub message from the evaluation step to roll back if the metric is below threshold.
Why it's wrong here
This approach deploys unconditionally and then attempts to roll back, which introduces unnecessary risk and complexity. It also requires additional infrastructure (Cloud Function, Pub/Sub) not integrated with the pipeline, and rollback may not be instantaneous, potentially serving a poor model temporarily.
✗
Use a Vertex AI Model Evaluation component and set its 'deploy' flag to true, which automatically deploys if the metric meets the threshold.
Why it's wrong here
The Vertex AI Model Evaluation component evaluates a model but does not deploy it. Deployment is a separate step, and there is no 'deploy' flag that triggers automatic deployment based on evaluation results. Conditional logic must be explicitly defined in the pipeline.
✓
Use a Vertex AI Pipelines condition with a comparison of the metric output to the threshold, and place the deployment component inside the condition's 'then' branch.
Why this is correct
Vertex AI Pipelines supports dsl.Condition to control execution flow based on pipeline parameters or component outputs. By comparing the evaluation metric to a threshold, the deployment component runs only when the condition is true. This is the standard approach for conditional steps in Kubeflow Pipelines.

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PMLE Automating and Orchestrating ML Pipelines Practice Question

Why each option matters

Option-by-option breakdown

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