- A
AutoMLTabularTrainingJobRunOp
Why wrong: AutoML includes tuning, but the component is specific to AutoML.
- B
CustomTrainingJobRunOp with hyperparameter arguments.
Why wrong: Custom training does not automatically perform hyperparameter tuning.
- C
ModelTrainComponent
Why wrong: This is for standard training, not tuning.
- D
HyperparameterTuningJobRunOp
This is the correct pre-built component for tuning.
Question 305 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An ML team wants to run a hyperparameter tuning job on Vertex AI using a pre-built pipeline component. Which component should they use?
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
✓
HyperparameterTuningJobRunOp
The HyperparameterTuningJobRunOp is the correct pre-built Vertex AI pipeline component specifically designed to launch a hyperparameter tuning job. It wraps the Vertex AI HyperparameterTuningJob API, allowing you to specify the worker pool spec, metric target, and parameter specifications directly within a Kubeflow Pipelines (KFP) or Vertex AI Pipelines orchestration context.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AutoMLTabularTrainingJobRunOp
Why it's wrong here
AutoML includes tuning, but the component is specific to AutoML.
- ✗
CustomTrainingJobRunOp with hyperparameter arguments.
Why it's wrong here
Custom training does not automatically perform hyperparameter tuning.
- ✗
ModelTrainComponent
Why it's wrong here
This is for standard training, not tuning.
- ✓
HyperparameterTuningJobRunOp
Why this is correct
This is the correct pre-built component for tuning.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake on the Google PMLE exam is confusing the pre-built HyperparameterTuningJobRunOp with CustomTrainingJobRunOp that accepts hyperparameter arguments, but the latter requires manual tuning logic rather than leveraging the built-in hyperparameter tuning service.
Detailed technical explanation
How to think about this question
Under the hood, HyperparameterTuningJobRunOp creates a HyperparameterTuningJob resource that launches multiple trials, each running a custom training job with different hyperparameter values sampled from the specified search space. The component automatically handles trial management, early stopping (e.g., using median stopping or Vizier algorithms), and returns the best trial's hyperparameters and model artifact. In a real-world scenario, you would chain this component with a model evaluation step to select the optimal configuration before deploying to production.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Automating and Orchestrating ML Pipelines — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: HyperparameterTuningJobRunOp — The HyperparameterTuningJobRunOp is the correct pre-built Vertex AI pipeline component specifically designed to launch a hyperparameter tuning job. It wraps the Vertex AI HyperparameterTuningJob API, allowing you to specify the worker pool spec, metric target, and parameter specifications directly within a Kubeflow Pipelines (KFP) or Vertex AI Pipelines orchestration context.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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