PMLE Automating and Orchestrating ML Pipelines Practice Question
A machine learning engineer is building a Vertex AI pipeline that uses a pre-built Google Cloud Pipeline Components (GCPC) to train a custom model. Which component should the engineer use to submit a custom training job to Vertex AI?
⚠ Common exam trap
A common mix-up: candidates confuse HyperparameterTuningJob with CustomJob because both involve training, but HyperparameterTuningJob is for multi-trial optimization, not a single training run, and ModelDeploy is a distractor that does not exist as a GCPC component.
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
✓
CustomJob
The CustomJob component is the correct choice because it is the pre-built GCPC component specifically designed to submit a custom training job to Vertex AI. It allows the engineer to specify a custom container image or a Python training script, along with machine configuration and hyperparameters, directly within a Vertex AI pipeline. Other components serve different purposes, such as hyperparameter tuning, batch predictions, or model deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
HyperparameterTuningJob
Why it's wrong here
HyperparameterTuningJob runs trials that search hyperparameters and returns the best configuration; it does not itself submit the custom training job the pipeline requires. It is tempting because tuning is part of model development, and it would be correct when the pipeline must optimise hyperparameters across multiple training runs.
- ✓
CustomJob
Why this is correct
The CustomJob component submits a custom training job to Vertex AI, letting the pipeline run bespoke training code within a managed job rather than a pre-built trainer. It satisfies the requirement to launch a custom training workload as a pipeline step.
- ✗
BatchPredictionJob
Why it's wrong here
BatchPredictionJob submits an inference job that scores data with an already-trained model; it never launches training, so no custom model is produced. It is tempting because it is a Vertex AI job component in the same GCPC library, and it would be the correct component when the pipeline must run batch predictions against an existing model.
- ✗
ModelDeploy
Why it's wrong here
ModelDeploy uploads and serves a trained model as a Vertex AI endpoint; it does not submit training jobs. It is tempting because deployment is the step after training, and it would be correct once a model artefact already exists and needs online prediction serving.
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Same concept, more angles
1 more way this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer wants to use a pre-built Google Cloud Pipeline Components (GCPC) to train a model using Vertex AI. Which component should they use?
easy- A.AutoMLTabularTrainingJobRunOp
- ✓ B.VertexTrainJobOp
- C.VertexEndpointCreateOp
- D.VertexBatchPredictOp
Why B: The Google Cloud Pipeline Components library includes pre-built components for various Vertex AI services. For training, the correct component is VertexTrainJobOp. VertexBatchPredictOp is for batch prediction, VertexEndpointCreateOp for deploying endpoints, and AutoMLTabularTrainingJobRunOp is specifically for AutoML tabular jobs.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.