- A
Dataflow pipeline to clean the new data before training
Why wrong: Data cleaning may be needed but is not essential for the core retrain-on-degradation logic.
- B
Vertex AI Evaluation component to compute model performance metrics on the validation set
Evaluation is needed to compare against the production model.
- C
Cloud Functions to trigger the pipeline when new data arrives in Cloud Storage
Cloud Functions can respond to Cloud Storage events and start the pipeline.
- D
Vertex AI Model Registry alias update to promote the model if performance passes the threshold
Updating the alias automates deployment if criteria are met.
- E
Cloud Scheduler to run the pipeline on a fixed schedule
Why wrong: The requirement is event-driven (new data), not scheduled.
Question 181 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.
A company wants to implement a CI/CD pipeline for their ML models using Vertex AI. They need to automatically retrain the model when new data arrives, but only if the model performance on a validation set has degraded by more than 5% compared to the current production model. Which three services or components should they incorporate into the automated pipeline? (Choose three.)
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
✓
Vertex AI Evaluation component to compute model performance metrics on the validation set
Option B is correct because Vertex AI Evaluation component can be used within a pipeline to compute model performance metrics (e.g., accuracy, precision, recall) on a validation set. This allows the pipeline to compare the newly trained model's performance against the current production model's performance, enabling the conditional logic to check if degradation exceeds 5%.
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.
- ✗
Dataflow pipeline to clean the new data before training
Why it's wrong here
Data cleaning may be needed but is not essential for the core retrain-on-degradation logic.
- ✓
Vertex AI Evaluation component to compute model performance metrics on the validation set
Why this is correct
Evaluation is needed to compare against the production model.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Cloud Functions to trigger the pipeline when new data arrives in Cloud Storage
Why this is correct
Cloud Functions can respond to Cloud Storage events and start the pipeline.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Vertex AI Model Registry alias update to promote the model if performance passes the threshold
Why this is correct
Updating the alias automates deployment if criteria are met.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Scheduler to run the pipeline on a fixed schedule
Why it's wrong here
The requirement is event-driven (new data), not scheduled.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In Google Cloud, the distinction between event-driven triggers (Cloud Functions/Eventarc) and scheduled triggers (Cloud Scheduler) is commonly tested. Candidates often mistakenly choose Cloud Scheduler when the requirement is for an event-driven retraining pipeline triggered by new data arrival.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Pipelines uses Kubeflow Pipelines SDK to orchestrate components; the Evaluation component leverages the `google_cloud_pipeline_components` library to run evaluation jobs that output metrics as Artifacts. These metrics can be compared using conditional nodes (e.g., `if` statements in the pipeline DSL) to decide whether to update the Model Registry alias. In a real-world scenario, a common subtlety is that the evaluation threshold check must be implemented as a custom Python component or using the `VertexAICondition` operator, as the Evaluation component itself does not enforce thresholds.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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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: Vertex AI Evaluation component to compute model performance metrics on the validation set — Option B is correct because Vertex AI Evaluation component can be used within a pipeline to compute model performance metrics (e.g., accuracy, precision, recall) on a validation set. This allows the pipeline to compare the newly trained model's performance against the current production model's performance, enabling the conditional logic to check if degradation exceeds 5%.
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
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