This PMLE practice question tests your understanding of monitoring 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.
A real-time recommendation model deployed on Vertex AI Endpoints is experiencing increased latency, especially during peak hours. The model is hosted on a single machine with 4 CPUs. Which set of actions should you take to diagnose and resolve the issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable autoscaling on the endpoint and analyze request patterns to set min/max instances.
Option C is correct because enabling autoscaling on a Vertex AI Endpoint allows the deployment to dynamically adjust the number of serving instances based on real-time traffic, directly addressing peak-hour latency. Analyzing request patterns to set appropriate min/max instances ensures that the endpoint scales proactively without over-provisioning, which is the standard diagnostic and resolution approach for latency issues caused by insufficient capacity under variable load.
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.
✗
Increase the machine type to with 32 CPUs and disable autoscaling.
Why it's wrong here
Fixed scaling is inefficient; peak loads may still exceed capacity.
✗
Switch the endpoint to use GPUs and enable batch requests.
Why it's wrong here
GPUs may not reduce latency if the model is not optimized; batch requests increase latency for real-time.
✓
Enable autoscaling on the endpoint and analyze request patterns to set min/max instances.
Why this is correct
Autoscaling handles peak load efficiently.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the serving framework to use TensorFlow Serving with gRPC.
Why it's wrong here
gRPC helps but does not address peak traffic scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling up (vertical scaling) or changing frameworks is the first step to fix latency, when the correct approach is to first diagnose capacity constraints and then scale out horizontally with autoscaling.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints use a managed infrastructure where autoscaling is based on CPU utilization or request count; setting min/max instances prevents cold-start latency by keeping a baseline of warm instances while allowing scale-out to handle bursts. Under the hood, the endpoint's load balancer distributes requests across instances, and without autoscaling, a single 4-CPU machine becomes CPU-bound, causing queuing delays that increase p99 latency. In real-world scenarios, analyzing request patterns (e.g., daily seasonality) allows setting min instances to match baseline traffic and max instances to cap costs, avoiding both under-provisioning and runaway scaling.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable autoscaling on the endpoint and analyze request patterns to set min/max instances. — Option C is correct because enabling autoscaling on a Vertex AI Endpoint allows the deployment to dynamically adjust the number of serving instances based on real-time traffic, directly addressing peak-hour latency. Analyzing request patterns to set appropriate min/max instances ensures that the endpoint scales proactively without over-provisioning, which is the standard diagnostic and resolution approach for latency issues caused by insufficient capacity under variable load.
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.
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Question Discussion
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