- A
Cloud Scheduler to trigger the pipeline on a cron schedule (e.g., first day of month)
Correct. Cloud Scheduler can trigger the pipeline on a cron schedule, satisfying condition (2) (first day of month).
- B
Monitoring alerts from Cloud Monitoring to trigger a Cloud Function that starts the pipeline
Why wrong: Incorrect. Cloud Monitoring alerts monitor infrastructure metrics, not model performance metrics like accuracy or F1 score. It cannot directly trigger the pipeline for condition (3) (performance degradation).
- C
Cloud Build trigger on code push to repository
Why wrong: Incorrect. Cloud Build triggers on code push are for CI/CD pipelines, not for the conditions listed (new data, schedule, or model performance).
- D
BigQuery scheduled query to trigger the pipeline after data transformation
Why wrong: Incorrect. BigQuery scheduled queries are for data transformation tasks, not for triggering the pipeline based on the specified conditions.
- E
Cloud Storage Pub/Sub notifications to trigger a Cloud Function that starts the pipeline
Correct. Cloud Storage Pub/Sub notifications can be configured to fire a Cloud Function whenever new training data is uploaded, satisfying condition (1).
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is building a continuous training (CT) pipeline that retrains a model whenever one of the following conditions is met: (1) new training data is available in Cloud Storage, (2) it's the first day of the month, or (3) the model's performance degrades below a threshold. Which TWO mechanisms should they combine to trigger the pipeline?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Cloud Scheduler to trigger the pipeline on a cron schedule (e.g., first day of month)
Option A is correct because Cloud Scheduler can trigger the pipeline on a cron schedule, such as the first day of every month, directly satisfying condition (2). Option E is correct because Cloud Storage Pub/Sub notifications can be configured to fire a Cloud Function whenever new training data is uploaded, satisfying condition (1). Condition (3), performance degradation below a threshold, is not directly covered by any of the provided options; however, it could be handled separately via a custom solution like Vertex AI Model Monitoring that triggers the pipeline. Among the given choices, A and E are the best combination for the conditions that have direct native support.
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.
- ✓
Cloud Scheduler to trigger the pipeline on a cron schedule (e.g., first day of month)
Why this is correct
Correct. Cloud Scheduler can trigger the pipeline on a cron schedule, satisfying condition (2) (first day of month).
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Monitoring alerts from Cloud Monitoring to trigger a Cloud Function that starts the pipeline
Why it's wrong here
Incorrect. Cloud Monitoring alerts monitor infrastructure metrics, not model performance metrics like accuracy or F1 score. It cannot directly trigger the pipeline for condition (3) (performance degradation).
- ✗
Cloud Build trigger on code push to repository
Why it's wrong here
Incorrect. Cloud Build triggers on code push are for CI/CD pipelines, not for the conditions listed (new data, schedule, or model performance).
- ✗
BigQuery scheduled query to trigger the pipeline after data transformation
Why it's wrong here
Incorrect. BigQuery scheduled queries are for data transformation tasks, not for triggering the pipeline based on the specified conditions.
- ✓
Cloud Storage Pub/Sub notifications to trigger a Cloud Function that starts the pipeline
Why this is correct
Correct. Cloud Storage Pub/Sub notifications can be configured to fire a Cloud Function whenever new training data is uploaded, satisfying condition (1).
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse Cloud Monitoring (infrastructure metrics) with model performance monitoring. Cloud Monitoring does not natively evaluate model metrics like accuracy or F1 score, so using it to trigger retraining on performance degradation is incorrect.
Detailed technical explanation
How to think about this question
Cloud Scheduler uses Unix cron format (e.g., '0 0 1 * *' for midnight on the first day of every month) and sends HTTP requests to a target endpoint (e.g., Cloud Run, Cloud Functions, or HTTP-triggered AI Platform Pipelines). Cloud Storage Pub/Sub notifications are configured via the --notification-config flag in gsutil or via the Cloud Console, and they publish events to a Pub/Sub topic when objects are created, deleted, or updated; a Cloud Function subscribed to that topic can then start the pipeline. For condition (3), model performance degradation is typically detected by a separate evaluation job that runs after each deployment or on a schedule, and that job can itself trigger the pipeline via a Cloud Function or Cloud Scheduler.
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
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: Cloud Scheduler to trigger the pipeline on a cron schedule (e.g., first day of month) — Option A is correct because Cloud Scheduler can trigger the pipeline on a cron schedule, such as the first day of every month, directly satisfying condition (2). Option E is correct because Cloud Storage Pub/Sub notifications can be configured to fire a Cloud Function whenever new training data is uploaded, satisfying condition (1). Condition (3), performance degradation below a threshold, is not directly covered by any of the provided options; however, it could be handled separately via a custom solution like Vertex AI Model Monitoring that triggers the pipeline. Among the given choices, A and E are the best combination for the conditions that have direct native support.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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