PMLE Automating and Orchestrating ML Pipelines Practice Question
An ML pipeline must run a set of preprocessing tasks for each data shard in parallel. Which KFP SDK features should they use to implement this? (Choose two.)
⚠ Common exam trap
In the Google PMLE exam, note that dsl.ParallelFor is used for parallel iteration over data shards, and dsl.Collected gathers outputs from all iterations. Candidates often confuse these with dsl.Condition (for branching) or dsl.PipelineParam (for parameters), so carefully read whether the question asks for parallel processing or conditional logic.
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.ParallelFor
dsl.ParallelFor (A) is correct because it is the KFP SDK construct that iterates over a list (such as the set of data shards) and fans out the loop body into parallel task executions, which is exactly what is needed to run preprocessing per shard concurrently. dsl.Collected (C) is correct because it is used with dsl.ParallelFor to gather the outputs of all parallel iterations into a single list, allowing downstream pipeline steps to consume the aggregated results of the per-shard preprocessing. dsl.PipelineParam (B) is not the mechanism for parallel iteration; it only represents a runtime parameter passed into a pipeline or component. dsl.Condition (D) implements conditional branching (if/else) rather than parallel fan-out, and dsl.ExitHandler (E) defines cleanup logic that runs when a scope exits, neither of which provides the required parallel execution over shards.
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 this is correct
dsl.ParallelFor iterates over the shard list at pipeline-compile time, generating one preprocessing task instance per shard that KFP schedules concurrently. This directly satisfies the requirement to run preprocessing tasks for each data shard in parallel.
- ✗
dsl.PipelineParam
Why it's wrong here
dsl.PipelineParam passes a single value into a pipeline run; it cannot fan out work across shards. It is tempting because parameterising pipelines is genuinely useful for reusable, configurable workflows, but parallel per-shard execution requires dsl.ParallelFor to iterate the shard list and spawn concurrent tasks.
- ✓
dsl.Collected
Why this is correct
dsl.Collected wraps the output of a dsl.ParallelFor loop, gathering each shard's task outputs into one list for downstream consumption. It satisfies the fan-in requirement after parallel preprocessing, letting subsequent pipeline steps iterate over all shard results without manual aggregation.
- ✗
dsl.Condition
Why it's wrong here
dsl.Condition branches on a boolean predicate, executing one path or another; it cannot iterate a shard collection to launch concurrent tasks. It is tempting because conditional branching is genuinely useful for optional pipeline steps, but the scenario needs dsl.ParallelFor to fan out identical preprocessing tasks across every shard.
- ✗
dsl.ExitHandler
Why it's wrong here
dsl.ExitHandler runs a cleanup or notification task after a pipeline scope completes or fails; it does not iterate shards or spawn parallel tasks. It is tempting because exit handlers are genuinely useful for teardown and error notification, but per-shard parallelism requires dsl.ParallelFor to iterate the shard list.
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