- A
Conditional component that checks evaluation metrics and promotes if successful
A conditional gate based on metrics decides whether to promote to production.
- B
Dataflow job to preprocess data
Why wrong: Data preprocessing is typically part of the training pipeline, not delivery.
- C
ModelDeploymentOp to deploy to staging
This deploys the model to a staging endpoint for testing.
- D
Importer component to bring in the model
Why wrong: The model artifact is already produced by the training pipeline; importer is unnecessary.
- E
A/B testing component to split traffic
Why wrong: A/B testing is a more advanced pattern; not required for basic continuous delivery.
Question 313 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 organization wants to implement continuous delivery for their ML model. After a new model is trained and evaluated, they want to automatically deploy it to a staging endpoint, run validation tests, and if passed, promote to production. Which two components should they include in their delivery pipeline? (Choose two.)
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
✓
Conditional component that checks evaluation metrics and promotes if successful
Option A is correct because a conditional component that checks evaluation metrics (e.g., accuracy, precision, recall) against a predefined threshold is essential for automated promotion. This component acts as a gate, ensuring only models that meet quality criteria are promoted to production, which is a core requirement for continuous delivery in ML pipelines.
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.
- ✓
Conditional component that checks evaluation metrics and promotes if successful
Why this is correct
A conditional gate based on metrics decides whether to promote to production.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Dataflow job to preprocess data
Why it's wrong here
Data preprocessing is typically part of the training pipeline, not delivery.
- ✓
ModelDeploymentOp to deploy to staging
Why this is correct
This deploys the model to a staging endpoint for testing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Importer component to bring in the model
Why it's wrong here
The model artifact is already produced by the training pipeline; importer is unnecessary.
- ✗
A/B testing component to split traffic
Why it's wrong here
A/B testing is a more advanced pattern; not required for basic continuous delivery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In Google Cloud ML pipelines, the key distinction is between deployment components (like ModelDeploymentOp in Vertex AI Pipelines) and data processing components (like Dataflow jobs). Candidates often mistakenly include preprocessing steps in the deployment pipeline instead of focusing on the deployment and validation logic.
Detailed technical explanation
How to think about this question
In Kubeflow Pipelines, a conditional component uses the `kfp.dsl.Condition` to evaluate metrics (e.g., from an `EvaluateModel` step) and conditionally execute downstream steps like `ModelDeploymentOp`. The `ModelDeploymentOp` typically wraps a Kubernetes Deployment or a Vertex AI endpoint creation, allowing the model to be served on a staging endpoint for validation. This pattern ensures that only validated models are promoted, reducing risk in production rollouts.
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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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: Conditional component that checks evaluation metrics and promotes if successful — Option A is correct because a conditional component that checks evaluation metrics (e.g., accuracy, precision, recall) against a predefined threshold is essential for automated promotion. This component acts as a gate, ensuring only models that meet quality criteria are promoted to production, which is a core requirement for continuous delivery in ML pipelines.
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.
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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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