- A
Create or update the pipeline in Vertex AI using the compiled file
Use gcloud or Python client to register the pipeline in Vertex AI.
- B
Upload the compiled pipeline to Cloud Storage
The compiled pipeline file needs to be stored in GCS for Vertex AI to access.
- C
Run the pipeline immediately after deployment
Why wrong: Running is a separate action; CI/CD typically only deploys, not runs.
- D
Install KFP SDK and compile the pipeline
The pipeline must be compiled to a JSON/YAML file using the KFP compiler.
- E
Configure Cloud Scheduler to trigger on push
Why wrong: Cloud Scheduler is for time-based schedules, not event-driven triggers from Cloud Build.
Question 257 of 1,000
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.
A team wants to implement CI/CD for their ML pipeline using Cloud Build. They want to automatically compile and deploy the pipeline when code is pushed to the main branch. Which three steps should they include in the Cloud Build configuration? (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
✓
Create or update the pipeline in Vertex AI using the compiled file
Option A is correct because the Cloud Build configuration should include a step to create or update the pipeline in Vertex AI using the compiled file. This step registers the pipeline definition with Vertex AI Pipelines, making it available for execution and versioning. Without this, the compiled pipeline would not be accessible for deployment or scheduling within the Vertex AI environment.
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.
- ✓
Create or update the pipeline in Vertex AI using the compiled file
Why this is correct
Use gcloud or Python client to register the pipeline in Vertex AI.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Upload the compiled pipeline to Cloud Storage
Why this is correct
The compiled pipeline file needs to be stored in GCS for Vertex AI to access.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Run the pipeline immediately after deployment
Why it's wrong here
Running is a separate action; CI/CD typically only deploys, not runs.
- ✓
Install KFP SDK and compile the pipeline
Why this is correct
The pipeline must be compiled to a JSON/YAML file using the KFP compiler.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure Cloud Scheduler to trigger on push
Why it's wrong here
Cloud Scheduler is for time-based schedules, not event-driven triggers from Cloud Build.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is confusing build-time actions (compilation, upload, registration) with runtime actions (execution, scheduling). Candidates often mistakenly include immediate pipeline execution as a CI/CD step instead of focusing on deploying and registering the pipeline artifact.
Detailed technical explanation
How to think about this question
Under the hood, the KFP SDK compiles a pipeline into a JSON or YAML format that defines the DAG of components and their dependencies. Uploading this compiled file to Cloud Storage ensures it is persisted and can be referenced by Vertex AI Pipelines for execution. The create or update step in Vertex AI uses the `pipeline_jobs.create` API to register the pipeline, allowing versioning and enabling triggers like Cloud Scheduler to launch runs based on the latest compiled definition.
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: Create or update the pipeline in Vertex AI using the compiled file — Option A is correct because the Cloud Build configuration should include a step to create or update the pipeline in Vertex AI using the compiled file. This step registers the pipeline definition with Vertex AI Pipelines, making it available for execution and versioning. Without this, the compiled pipeline would not be accessible for deployment or scheduling within the Vertex AI environment.
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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