PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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.
Refer to the exhibit. A data engineer sees these metrics from Cloud Monitoring for a deployed Vertex AI Endpoint. What is the most effective action to reduce latency?
Why wrong: Batch prediction is not for reducing online latency; it is for asynchronous processing.
B
Increase the number of replicas
Adding replicas scales horizontally, reducing load per replica and improving latency.
C
Reduce the machine type
Why wrong: Reducing machine type would likely worsen the issue.
D
Enable model quantization
Why wrong: Quantization can reduce model size and improve inference speed, but the immediate problem is high CPU utilization due to traffic; scaling is more direct.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of replicas
The metrics show high CPU utilization and increasing latency, indicating the current instance is overloaded. Increasing the number of replicas distributes the inference requests across multiple instances, reducing per-replica load and lowering response times. This is the most direct way to scale horizontally and address latency caused by resource saturation.
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.
✗
Switch to batch prediction
Why it's wrong here
Batch prediction is not for reducing online latency; it is for asynchronous processing.
✓
Increase the number of replicas
Why this is correct
Adding replicas scales horizontally, reducing load per replica and improving latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the machine type
Why it's wrong here
Reducing machine type would likely worsen the issue.
✗
Enable model quantization
Why it's wrong here
Quantization can reduce model size and improve inference speed, but the immediate problem is high CPU utilization due to traffic; scaling is more direct.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that model optimization (quantization) or switching to batch mode is the primary fix for latency, when the metrics clearly point to a scaling bottleneck.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints use autoscaling based on CPU utilization or request count, but scaling policies have cooldown periods. Under the hood, each replica runs a container serving the model via gRPC or HTTP; increasing replicas allows the load balancer to distribute requests using round-robin or least-connections algorithms. In real-world scenarios, sudden traffic spikes can overwhelm a single replica before autoscaling kicks in, so preemptively increasing the minimum replica count is a common mitigation.
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.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of replicas — The metrics show high CPU utilization and increasing latency, indicating the current instance is overloaded. Increasing the number of replicas distributes the inference requests across multiple instances, reducing per-replica load and lowering response times. This is the most direct way to scale horizontally and address latency caused by resource saturation.
What should I do if I get this PDE 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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