PMLE Scaling Prototypes into ML Models Practice Question
An ML engineer is using Vertex AI Pipelines to orchestrate a training workflow. The pipeline includes a step that trains a model and a subsequent step that evaluates the model. The engineer wants to ensure that the evaluation step runs only if the training step succeeds and that the pipeline fails if the model's accuracy is below a threshold. Which approach should the engineer use?
⚠ Common exam trap
The trap here is assuming that Vertex AI Pipelines automatically fails on low accuracy metrics or that Model Monitoring can be used within a pipeline for this purpose.
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 in the pipeline that checks the training step's status and a custom component that raises an exception if accuracy is below the threshold.
To conditionally run a step and fail the pipeline based on a metric, the engineer should use a condition to check the training step's status and a custom component that raises an exception if accuracy is too low. Vertex AI Pipelines conditions allow steps to run only if previous steps succeed, and raising an exception in a component causes the pipeline to fail. This combination provides the required control flow and failure semantics.
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 evaluation step's retry policy to zero and use the pipeline's built-in accuracy threshold parameter.
Why it's wrong here
Vertex AI Pipelines does not have a built-in accuracy threshold parameter. Retry policies control how many times a step is retried on failure, not conditional failure based on metrics. This option misrepresents the capabilities of the service and would not enforce the desired behavior.
- ✓
Use a condition in the pipeline that checks the training step's status and a custom component that raises an exception if accuracy is below the threshold.
Why this is correct
Vertex AI Pipelines supports conditions to control step execution based on the status of previous steps. A custom component can evaluate the model and raise an exception if the accuracy does not meet the threshold. This exception will cause the pipeline to fail, ensuring that only models meeting the criteria proceed. This approach provides the required control flow and failure behavior.
- ✗
Use a Vertex AI Model resource to store the model and enable Model Monitoring to trigger a pipeline failure if accuracy drops.
Why it's wrong here
Model Monitoring is for deployed models and detects drift, not for evaluating model accuracy during a pipeline run. It cannot trigger a pipeline failure based on a static accuracy threshold. This option conflates monitoring with pipeline control flow and does not meet the requirement.
- ✗
Configure the evaluation step to always run and rely on Vertex AI Pipelines to automatically fail the pipeline if the accuracy metric is below the threshold.
Why it's wrong here
Vertex AI Pipelines does not automatically fail based on metric values unless you explicitly implement a check. The evaluation step would run regardless of training success, and without a custom check, the pipeline would not fail for low accuracy. This approach lacks the necessary logic to enforce the threshold.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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