Question 432 of 990
easyMultiple SelectObjective-mapped
Best Practices for Vertex AI Model Deployment
Which TWO are best practices for deploying models to Vertex AI Prediction? (Choose 2.)
Quick Answer
The answer is to use a dedicated service account with minimal permissions for the endpoint and to leverage version aliases for easy rollback. These are best practices for deploying models to Vertex AI because a dedicated, least-privilege service account enforces the principle of least privilege, limiting the blast radius if credentials are compromised, while version aliases allow you to point traffic to a specific model version without changing the endpoint configuration, enabling seamless rollbacks and canary deployments. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of secure and resilient deployment patterns, often appearing as a trap where you must distinguish between security hygiene and operational convenience—common distractors include logging all inputs (which risks PII exposure) or insisting on identical environments (which is impractical). A helpful memory tip: think “lock it down, then alias it out”—secure the endpoint with a minimal service account, then manage versions with aliases for safe updates.
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
✓
Monitor prediction latency and error rates with Cloud Monitoring alerts.
The correct answers are A and C. Option A is a best practice because monitoring prediction latency and error rates with Cloud Monitoring alerts helps detect performance issues and ensure availability. Option C is a best practice because using a dedicated service account with minimal permissions follows the principle of least privilege, enhancing security. Option B is not a best practice because logging all raw inputs and outputs for every request can cause privacy concerns, increase costs, and is unnecessary for most use cases. Option D is not a best practice because deploying in the same environment as training may not always be feasible; instead, models should be containerized to ensure consistency across environments. Option E is not a best practice because using the default alias 'default' for all deployments can lead to confusion and makes rollbacks more difficult; proper versioning and aliases should be used.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Monitor prediction latency and error rates with Cloud Monitoring alerts.
Why this is correct
Essential for detecting performance issues.
- ✗
Log all raw prediction inputs and outputs for every request for auditing.
Why it's wrong here
May contain PII and impact performance; sample logging is better.
- ✓
Use a dedicated service account with minimal permissions for the endpoint.
Why this is correct
Principle of least privilege.
- ✗
Always deploy the model in the same environment as training to avoid incompatibility.
Why it's wrong here
While important, the training environment may not have the same serving libraries; use consistent versions but not identical.
- ✗
Use the default model version alias 'default' for all deployments to simplify updates.
Why it's wrong here
Should use custom aliases for canary testing.
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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. Which TWO options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)
easy- A.Use a larger machine type with more memory
- ✓ B.Optimize the model using quantization or pruning
- ✓ C.Deploy the model in the same region as the clients
- D.Use batch prediction instead of online prediction
- E.Enable model caching at the endpoint
Why B: Options B and C are correct. Optimizing the model using quantization or pruning reduces the model's size and computational requirements, directly decreasing per-request latency. Deploying the model in the same region as the clients minimizes network round-trip time, reducing overall serving latency. Option A (larger machine type) may increase throughput but does not necessarily reduce latency per request; Option D (batch prediction) is designed for high throughput, not low latency; Option E (model caching) is not a standard feature of Vertex AI endpoints for reducing latency.
Last reviewed: Jun 24, 2026
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