- A
Use a larger machine type with more memory
Why wrong: Larger machines may not reduce latency if the bottleneck is network or model inefficiency; they increase cost.
- B
Optimize the model using quantization or pruning
Reduces model size and inference time, lowering latency with minimal accuracy impact.
- C
Deploy the model in the same region as the clients
Reduces network round-trip time, directly lowering latency.
- D
Use batch prediction instead of online prediction
Why wrong: Batch prediction is not real-time and typically has higher latency for individual requests.
- E
Enable model caching at the endpoint
Why wrong: Vertex AI Endpoints do not have a built-in caching feature; caching at client side may help but is not a best practice on the endpoint.
Question 392 of 1,000
easyMultiple SelectObjective-mapped
PMLE Practice Question: Which TWO options are best practices for reducing…
This PMLE practice question tests your understanding of pmle exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)
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.
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
✓
Optimize the model using quantization or pruning
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.
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.
- ✗
Use a larger machine type with more memory
Why it's wrong here
Larger machines may not reduce latency if the bottleneck is network or model inefficiency; they increase cost.
- ✓
Optimize the model using quantization or pruning
Why this is correct
Reduces model size and inference time, lowering latency with minimal accuracy impact.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Authentication checks who the user is.
- ✓
Deploy the model in the same region as the clients
Why this is correct
Reduces network round-trip time, directly lowering latency.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Authentication checks who the user is.
- ✗
Use batch prediction instead of online prediction
Why it's wrong here
Batch prediction is not real-time and typically has higher latency for individual requests.
- ✗
Enable model caching at the endpoint
Why it's wrong here
Vertex AI Endpoints do not have a built-in caching feature; caching at client side may help but is not a best practice on the endpoint.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: Optimize the model using quantization or pruning — 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.
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.
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Last reviewed: Jun 24, 2026
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