hardMultiple ChoiceObjective-mapped
Vertex AI Endpoint Quota Fix in Pipelines
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?
Quick Answer
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.”
⚠ Common exam trap
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.
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
✓
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.
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.
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.
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Same concept, more angles
1 more way this is tested on PMLE
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: Adding retry logic with exponential backoff handles transient quota errors gracefully without manual intervention, making it a resilient and long-term solution for intermittent failures. Option A is wrong because longer wait times do not address the root cause of quota exhaustion. Option C is wrong because deploying on Compute Engine introduces additional complexity and does not resolve Vertex AI quota issues. Option D is wrong because requesting a permanent quota increase may be necessary for sustained high volume, but it requires planning and approval, and is not the best first step for intermittent errors.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.