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.
Network Topology
The exhibit shows a Cloud Build configuration. An ML engineer wants to automate the deployment of a model to Vertex AI after training. What is missing in this config to successfully deploy the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A step to build the serving container image
The Cloud Build configuration shown is for training a model, but to deploy it to Vertex AI, a serving container image must be built and pushed to Artifact Registry. Vertex AI requires a custom serving container (or a prebuilt one) to host the model for predictions. Without a step to build the serving container image (e.g., using a Dockerfile that includes the model and serving dependencies), the deployment will fail because there is no runnable image to deploy to the endpoint.
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.
✗
A step to upload the training image to Artifact Registry
Why it's wrong here
The config already pushes the training image.
✓
A step to build the serving container image
Why this is correct
The config only builds the training image; it needs a separate step to build and push the serving image.
Related concept
Read the scenario before looking for a memorised answer.
✗
A step to run unit tests
Why it's wrong here
Not required for deployment.
✗
A step to create the Vertex AI Endpoint
Why it's wrong here
Typically the endpoint is created once; deployment uses the model resource.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between training and serving containers, leading candidates to mistakenly think that the training image (or any image) is sufficient for deployment, when in fact a separate serving container is required.
Detailed technical explanation
How to think about this question
Vertex AI deploys models using either prebuilt containers (e.g., for TensorFlow, PyTorch) or custom containers. A custom serving container must include the model artifact, a web server (e.g., FastAPI, Flask), and a prediction handler that conforms to Vertex AI's HTTP request/response format (e.g., JSON with `instances` key). The Cloud Build config must have a step that builds this container, tags it with the model version, and pushes it to Artifact Registry before the `gcloud ai models upload` and `gcloud ai endpoints deploy` commands can reference it.
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.
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: A step to build the serving container image — The Cloud Build configuration shown is for training a model, but to deploy it to Vertex AI, a serving container image must be built and pushed to Artifact Registry. Vertex AI requires a custom serving container (or a prebuilt one) to host the model for predictions. Without a step to build the serving container image (e.g., using a Dockerfile that includes the model and serving dependencies), the deployment will fail because there is no runnable image to deploy to the endpoint.
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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Question Discussion
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