The answer is to modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint. This fix directly resolves the Vertex AI endpoint quota exceeded pipeline error by leveraging Vertex AI’s model versioning capability, which allows multiple model versions under a single endpoint without exceeding the regional quota of one endpoint. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI resource quotas and the distinction between creating a new endpoint versus deploying a new model version—a common trap is assuming you need to request a quota increase or redesign the pipeline. The key insight is that Vertex AI endpoints support versioned deployments, so reusing the existing endpoint with a new model version is the minimal, code-efficient fix. Memory tip: “Version, don’t create—one endpoint per region is your fate.”
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are an ML engineer at a logistics company. The company uses a Vertex AI Pipeline with BigQuery ML to train a model that predicts delivery delays based on weather, traffic, and historical order data. The pipeline runs daily and includes steps: (1) data extraction from BigQuery, (2) feature engineering using Dataflow, (3) model training with BigQuery ML (logistic regression), (4) model evaluation, and (5) conditional deployment to a Vertex AI Endpoint if accuracy > 0.85. Recently, the pipeline has been failing at step 5 with the error: "Vertex AI Endpoint creation failed: Quota limit of 1 endpoint per region exceeded." The company has already created one endpoint in the same region for another model. The pipeline is configured to create a new endpoint each time a model is deployed. The engineer needs to fix this with minimal changes to the pipeline code. Which course of action should the engineer take?
Submit a quota increase request to Google Cloud for Vertex AI Endpoints in the current region.
Why wrong: Not a code fix; may take days to approve.
B
Change the region in the pipeline configuration to a region with available endpoint quota.
Why wrong: May not be feasible and adds cross-region latency.
C
Remove the accuracy threshold and deploy every model automatically to a pre-created endpoint.
Why wrong: Deploys low-quality models and wastes resources.
D
Modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint instead of creating a new one.
Reuses the existing endpoint, avoiding quota limits.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint instead of creating a new one.
Option D is correct because it directly addresses the root cause: the pipeline fails because it tries to create a new endpoint each time, exceeding the regional quota of one endpoint. By modifying the deployment step to check for an existing endpoint and deploying a new model version to it, the engineer avoids quota issues without altering the pipeline's core logic or requiring external approvals. This approach leverages Vertex AI's model versioning capability, which allows multiple model versions under a single endpoint, aligning with minimal code changes.
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.
✗
Submit a quota increase request to Google Cloud for Vertex AI Endpoints in the current region.
Why it's wrong here
Not a code fix; may take days to approve.
✗
Change the region in the pipeline configuration to a region with available endpoint quota.
Why it's wrong here
May not be feasible and adds cross-region latency.
✗
Remove the accuracy threshold and deploy every model automatically to a pre-created endpoint.
Why it's wrong here
Deploys low-quality models and wastes resources.
✓
Modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint instead of creating a new one.
Why this is correct
Reuses the existing endpoint, avoiding quota limits.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may focus on quota limits as a resource issue (Option A) or a region issue (Option B), rather than recognizing that the pipeline's deployment logic is architecturally flawed by creating a new endpoint per deployment, which is both inefficient and violates best practices for model serving.
Detailed technical explanation
How to think about this question
Vertex AI endpoints support deploying multiple model versions (via `deployModel` API) to the same endpoint, enabling traffic splitting and canary deployments without consuming additional endpoint quota. The pipeline's current design creates a new endpoint per deployment, which is unnecessary and wasteful; reusing an existing endpoint with versioning is the standard practice for continuous deployment. In a real-world scenario, this pattern also simplifies rollback, as previous model versions remain available under the same endpoint.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint instead of creating a new one. — Option D is correct because it directly addresses the root cause: the pipeline fails because it tries to create a new endpoint each time, exceeding the regional quota of one endpoint. By modifying the deployment step to check for an existing endpoint and deploying a new model version to it, the engineer avoids quota issues without altering the pipeline's core logic or requiring external approvals. This approach leverages Vertex AI's model versioning capability, which allows multiple model versions under a single endpoint, aligning with minimal code changes.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this failure?
hard
A.Run the pipeline steps sequentially with longer wait times.
✓ B.Add retry logic with exponential backoff to the deployment step in the pipeline.
C.Switch to deploying models using a custom container on Compute Engine.
D.Request a permanent quota increase for Vertex AI Endpoints.
Why B: Option D is correct because implementing retry logic with exponential backoff is a resilient pattern for transient quota errors. Option A is wrong because increasing quota requires a support ticket and may not be granted immediately. Option B is wrong because using a custom container does not address quota limits. Option C is wrong because sequential execution does not prevent quota errors.
Last reviewed: Jun 11, 2026
Question Discussion
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