PMLE Automating and Orchestrating ML Pipelines Practice Question
A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?
⚠ Common exam trap
Test-takers frequently confuse `dsl.Condition` with `dsl.ExitHandler` because both involve decision-making, but `ExitHandler` is only for post-exit cleanup, not for branching based on step outputs.
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
✓
dsl.Condition
The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
dsl.ParallelFor
Why it's wrong here
dsl.ParallelFor iterates a task over a list concurrently, producing fan-out execution rather than branching. It cannot test whether metrics exceed a threshold. dsl.Condition evaluates a boolean expression and runs the deploy component only when that predicate is true, matching the conditional deployment requirement.
- ✗
dsl.ExitHandler
Why it's wrong here
dsl.ExitHandler registers a cleanup task that runs after a pipeline or block finishes, regardless of success or failure. It does not branch on metric values. dsl.Condition supplies the if-then gating needed to deploy only when evaluation metrics exceed the threshold.
- ✓
dsl.Condition
Why this is correct
dsl.Condition wraps pipeline steps in a conditional branch whose predicate is evaluated at runtime, so the deployment step executes only when the evaluation metrics exceed the threshold. This directly implements the stem's conditional deployment requirement within the KFP pipeline definition.
- ✗
dsl.Collected
Why it's wrong here
dsl.Collected is a type annotation for grouping a loop's output artefacts after a ParallelFor, not a branching construct. It cannot evaluate metrics or gate deployment. dsl.Condition wraps the deploy task with a boolean predicate comparing evaluation output against the threshold, which is the required behaviour.
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