PMLE Automating and Orchestrating ML Pipelines Practice Question

A pipeline uses the Google Cloud Pipeline Components to perform AutoML training and batch prediction. Which two components from the GCPC library should they use? (Choose two.)

⚠ Common exam trap

PMLE often tests the confusion between online prediction (EndpointPredictOp) and batch prediction (BatchPredictOp), and between custom training (CustomJobRunOp) and AutoML training (AutoMLTabularTrainingJobRunOp) — candidates pick the wrong op for the stated workload.

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

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AutoMLTabularTrainingJobRunOp

Option C, AutoMLTabularTrainingJobRunOp, is correct because it is the Google Cloud Pipeline Components (GCPC) operator that wraps the Vertex AI AutoML tabular training job, allowing the pipeline to launch an AutoMLTabularTrainingJob and produce a model artifact. Option D, BatchPredictOp, is correct because it is the GCPC component that submits a Vertex AI batch prediction job against a trained model and a specified input data source, which is exactly the batch prediction step described in the scenario. Option A, CustomJobRunOp, is not appropriate here because it runs a custom training container rather than an AutoML training job. Option B, DataflowPythonOp, is a Dataflow-based Python execution component and does not perform AutoML training or batch prediction. Option E, EndpointPredictOp, performs online prediction against a deployed Vertex AI endpoint, not batch prediction, so it does not fit the scenario.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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CustomJobRunOp
Why it's wrong here
CustomJobRunOp runs an arbitrary user-supplied container on Vertex AI, so it does not provide the managed AutoML training the pipeline requires. It is tempting because custom jobs suit bespoke training code or non-standard frameworks, but this scenario calls for a purpose-built AutoML training component rather than a generic container job.
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DataflowPythonOp
Why it's wrong here
DataflowPythonOp runs arbitrary Apache Beam Python code, not AutoML training or batch prediction, so it cannot invoke those managed services. It suits custom data transformation pipelines. AutoML training and prediction require the dedicated AutoML components instead.
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AutoMLTabularTrainingJobRunOp
Why this is correct
AutoMLTabularTrainingJobRunOp submits a tabular AutoML training job directly from the pipeline, satisfying the AutoML training requirement. It wraps the Vertex AI training API as a pipeline component, so the pipeline orchestrates training without custom code. Batch prediction is handled separately by a prediction component, making this one of the two required GCPC components.
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BatchPredictOp
Why this is correct
BatchPredictOp performs batch prediction against a trained model within a Vertex AI Pipelines workflow, satisfying the stem's batch prediction requirement. It consumes a model and input data, writing predictions to Cloud Storage, and pairs with AutoML training components in the GCPC library.
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EndpointPredictOp
Why it's wrong here
EndpointPredictOp invokes an existing deployed Vertex AI endpoint, so it cannot generate the batch predictions the pipeline requires from a freshly trained AutoML model. It is tempting because endpoint prediction is genuinely useful when serving real-time requests against an already-deployed model, but batch prediction needs a dedicated batch component instead.

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PMLE Automating and Orchestrating ML Pipelines Practice Question

Why each option matters

Option-by-option breakdown

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