- A
Cloud Storage for datasets
Why wrong: Datasets are not directly part of the CI/CD pipeline; they are consumed during training.
- B
Container Registry for model images
Model images must be stored and versioned in a registry like Container Registry to deploy to Vertex AI.
- C
Cloud Source Repositories
Why wrong: While useful for version control, it is not strictly essential for the CI/CD pipeline if code is stored elsewhere.
- D
Vertex AI Endpoints for deployment
Vertex AI Endpoints are the target for deploying the model image.
- E
Cloud Functions for triggers
Why wrong: Cloud Functions can trigger pipelines but are not essential; Cloud Build triggers can be used directly.
Question 293 of 1,000
Two Essential Components for ML CI/CD Pipelines
This PDE practice question tests your understanding of operationalizing machine learning models. 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 data engineer is setting up CI/CD for a machine learning model using Cloud Build and Vertex AI. Which two components are essential? (Select 2)
Quick Answer
The answer is Vertex AI Endpoints for deployment and Container Registry. These two components are essential for ML CI/CD pipelines because Container Registry stores the Docker images containing your trained model artifacts, while Vertex AI Endpoints provides the managed serving infrastructure to host and serve that model for predictions. On the Google Professional Data Engineer exam, this tests your understanding of how Cloud Build orchestrates the pipeline—pulling code, building the model image, pushing it to Container Registry, and then deploying to Vertex AI Endpoints—without needing separate compute or storage services for the deployment step. A common trap is selecting Cloud Storage instead of Container Registry, but remember that Cloud Storage holds raw data or model files, not the executable container images required for Vertex AI deployment. To recall this pairing, think of the pipeline as "build, store, serve": Cloud Build builds, Container Registry stores the image, and Vertex AI Endpoints serves it live.
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
✓
Container Registry for model images
Container Registry (option B) is essential because it stores the Docker container images that encapsulate the trained model and its dependencies, which Cloud Build builds and pushes to the registry. Vertex AI Endpoints (option D) is essential because it provides the managed serving infrastructure to deploy the model image and expose it as a REST API for online predictions, enabling the CI/CD pipeline to automatically update the endpoint with new model versions.
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 Storage for datasets
Why it's wrong here
Datasets are not directly part of the CI/CD pipeline; they are consumed during training.
- ✓
Container Registry for model images
Why this is correct
Model images must be stored and versioned in a registry like Container Registry to deploy to Vertex AI.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Source Repositories
Why it's wrong here
While useful for version control, it is not strictly essential for the CI/CD pipeline if code is stored elsewhere.
- ✓
Vertex AI Endpoints for deployment
Why this is correct
Vertex AI Endpoints are the target for deploying the model image.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Functions for triggers
Why it's wrong here
Cloud Functions can trigger pipelines but are not essential; Cloud Build triggers can be used directly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between 'essential' and 'optional' components; candidates mistakenly select Cloud Storage (A) because they think datasets are required for CI/CD, but the pipeline only needs the model image and a deployment target, not the raw training data.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Build uses a cloudbuild.yaml file to define steps: it pulls the source code, builds a Docker image with the model (e.g., using a custom prediction routine), pushes it to Container Registry (or Artifact Registry), and then deploys it to Vertex AI Endpoints using the `gcloud ai endpoints deploy-model` command. A subtle behavior is that Vertex AI Endpoints support canary deployments and traffic splitting, allowing CI/CD to gradually roll out new model versions without downtime. In a real-world scenario, a data engineer might configure Cloud Build to automatically retrain the model on new data, build a new image, and deploy it to the same endpoint with a new version ID, ensuring continuous delivery of updated predictions.
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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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Container Registry for model images — Container Registry (option B) is essential because it stores the Docker container images that encapsulate the trained model and its dependencies, which Cloud Build builds and pushes to the registry. Vertex AI Endpoints (option D) is essential because it provides the managed serving infrastructure to deploy the model image and expose it as a REST API for online predictions, enabling the CI/CD pipeline to automatically update the endpoint with new model versions.
What should I do if I get this PDE 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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