- A
Monitor prediction latency and error rates with Cloud Monitoring alerts.
Essential for detecting performance issues.
- B
Log all raw prediction inputs and outputs for every request for auditing.
Why wrong: May contain PII and impact performance; sample logging is better.
- C
Use a dedicated service account with minimal permissions for the endpoint.
Principle of least privilege.
- D
Always deploy the model in the same environment as training to avoid incompatibility.
Why wrong: While important, the training environment may not have the same serving libraries; use consistent versions but not identical.
- E
Use the default model version alias 'default' for all deployments to simplify updates.
Why wrong: Should use custom aliases for canary testing.
Question 432 of 1,000
easyMultiple SelectObjective-mapped
Best Practices for Vertex AI Model Deployment
This PMLE practice question tests your understanding of pmle exam topics. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Which TWO are best practices for deploying models to Vertex AI Prediction? (Choose 2.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Key principle: Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access.
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Authentication checks who the user is.
- ✗
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Authentication checks who the user is.
- ✗
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.
Common exam traps
Common exam trap: authentication is not authorization
Logging in proves the user can authenticate. It does not automatically mean the user is allowed to enter privileged or configuration mode. Watch for AAA authorization, privilege level and command authorization details.
Detailed technical explanation
How to think about this question
This kind of question is testing the difference between identity and permission. A user may successfully log in to a router because authentication is working, but still fail to enter configuration mode because authorization is missing, misconfigured or mapped to a lower privilege level.
KKey Concepts to Remember
- Authentication checks who the user is.
- Authorization controls what the user is allowed to do after login.
- Privilege levels affect access to EXEC and configuration commands.
- AAA, TACACS+ and RADIUS can separate login success from command access.
TExam Day Tips
- Do not assume successful login means full administrative access.
- Look for words such as cannot enter configuration mode, privilege level, authorization or command access.
- Separate login problems from permission problems before choosing the answer.
Key takeaway
Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review Cisco AAA concepts — authentication, authorization, and accounting. Study privilege levels (0–15), command authorization under TACACS+, and how RADIUS differs. Then practise related PMLE questions on access control and AAA configuration.
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FAQ
Questions learners often ask
What does this PMLE question test?
Authentication checks who the user is.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PMLE question wrong?
Review Cisco AAA concepts — authentication, authorization, and accounting. Study privilege levels (0–15), command authorization under TACACS+, and how RADIUS differs. Then practise related PMLE questions on access control and AAA configuration.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Authentication checks who the user is.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways 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.
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Last reviewed: Jun 24, 2026
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