PMLE Automating and Orchestrating ML Pipelines Practice Question
A company uses Vertex AI Pipelines to train models on a daily schedule. The pipeline includes a component that runs a BigQuery query to extract features. The team wants to ensure that if the BigQuery component fails due to transient network errors, the pipeline automatically retries it. How can they configure retries in Vertex AI Pipelines?
⚠ Common exam trap
It's easy for candidates to confuse Vertex AI Pipelines' native `retry` parameter with external retry mechanisms (Cloud Functions, Airflow) or misuse pipeline control flow constructs like `dsl.If` for retry logic, when the correct approach is a simple parameter on the component definition.
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
✓
Set the `retry` parameter of the component to a positive integer, for example `retry=3`.
Vertex AI Pipelines natively supports a `retry` parameter on pipeline components. Setting `retry=3` instructs the pipeline to automatically retry the component up to three times if it fails due to transient errors, such as network timeouts. This is the simplest and most direct way to handle retries within the Vertex AI Pipelines orchestration framework.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the component as a Cloud Function and configure Cloud Functions retry.
Why it's wrong here
Cloud Functions retry applies to event-driven function invocations, not to a component executing within a Vertex AI pipeline, so the pipeline's task still fails without retry. It is tempting because serverless retries are simple to enable, and this would suit standalone event-triggered processing outside Vertex AI Pipelines.
- ✗
Wrap the component in a `dsl.If` conditional that checks for failure and re-submits the component.
Why it's wrong here
A dsl.If conditional branches on a pipeline parameter or upstream output, not on runtime task failure, so it cannot detect the BigQuery error and re-submit. It is tempting because conditionals control flow, and this would suit choosing between components based on a known input value.
- ✗
Use Cloud Composer with a task retry policy in Airflow.
Why it's wrong here
Cloud Composer orchestrates Airflow DAGs externally; it does not configure retry behaviour inside a Vertex AI Pipelines component, so the pipeline's own failure handling is unchanged. It is tempting because Airflow retry policies are familiar, and this would suit pipelines already authored as Airflow DAGs.
- ✓
Set the `retry` parameter of the component to a positive integer, for example `retry=3`.
Why this is correct
The retry parameter on a task or component specifies how many times Vertex AI Pipelines re-executes it after failure, so retry=3 automatically reruns the BigQuery component on transient network errors. This satisfies the stem's automatic-retry requirement.
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