PMLE Automating and Orchestrating ML Pipelines Practice Question
A company runs a Vertex AI Pipeline that trains a model and then deploys it to a Vertex AI Endpoint. The pipeline uses a conditional deployment step based on the model's evaluation metric. The team wants to ensure that if the evaluation metric falls below a threshold, the pipeline fails and no deployment occurs. Which approach should they use?
⚠ Common exam trap
The trap here is believing that a Condition alone can fail the pipeline or that a built-in fail-fast policy exists, when in fact you must explicitly cause a failure in the else branch.
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 Condition to check the metric, and in the else branch, include a component that raises an exception or returns a failure status.
To conditionally deploy and fail the pipeline when the metric is unsatisfactory, the pipeline should use a Condition with an else branch that triggers a failing component. This ensures that deployment only happens when the metric passes, and the pipeline fails otherwise. Other methods either do not cause failure or rely on unsupported features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a custom component that evaluates the metric and, if below threshold, calls the Vertex AI API to cancel the pipeline.
Why it's wrong here
While it is technically possible to cancel a pipeline programmatically, this is an anti-pattern. It introduces race conditions and complexity. The pipeline would already be running, and cancellation might not prevent subsequent steps. A conditional failure component is simpler and more reliable.
- ✓
Use a Condition to check the metric, and in the else branch, include a component that raises an exception or returns a failure status.
Why this is correct
This approach ensures that if the metric is below threshold, the else branch executes a component that fails the pipeline. If the metric is above threshold, the deployment component runs. This provides both conditional deployment and a clear failure signal when the metric does not meet requirements.
- ✗
Configure the pipeline to use a fail-fast strategy by setting the pipeline's failure_policy to FAIL_FAST.
Why it's wrong here
Vertex AI Pipelines does not have a failure_policy parameter that can be set to FAIL_FAST. The pipeline's behavior on failure is determined by the components' exit statuses. There is no built-in setting to automatically fail the pipeline based on a metric value; you must implement it explicitly.
- ✗
Use a Condition in the pipeline that checks if the metric is greater than the threshold, and only then execute the deployment component.
Why it's wrong here
A Condition can skip the deployment component if the metric is below threshold, but it does not cause the pipeline to fail. The pipeline would still complete successfully, which may not be desired if the team wants a clear failure signal. To fail the pipeline, an additional step is needed.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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