- A
Implement a feature engineering pipeline that compresses the input features to reduce data size and inference time.
Why wrong: While potentially beneficial, this is a longer-term solution and does not provide immediate latency relief during the surge.
- B
Deploy a newer version of the model that uses a more efficient architecture to reduce computational complexity.
Why wrong: Deploying a new model requires time for development, testing, and approval, and may not be feasible for immediate mitigation.
- C
Increase the number of TensorFlow Serving instances by reducing the CPU request per pod in GKE to allow more pods per node.
Why wrong: Reducing CPU requests may lead to CPU starvation and pod instability, harming latency further.
- D
Add more nodes to the GKE cluster to increase the total CPU resources available for serving.
Adding nodes increases compute capacity, allowing more parallel inference and reducing latency under high load.
Quick Answer
The answer is to add more nodes to the GKE cluster to increase total CPU resources. This is correct because the latency spike is driven by CPU saturation at 95% utilization under a surge from 500 to 1,200 requests per second, while memory remains at 60%—a classic sign that compute, not memory, is the bottleneck. TensorFlow Serving on GKE relies on sufficient CPU cores to process inference requests in parallel; when CPU is exhausted, requests queue up, causing P99 latency to spike. On the Google Professional Machine Learning Engineer exam, this scenario tests your ability to distinguish between scaling compute resources versus optimizing model code or configuration—a common trap is to suggest model quantization or batching, but since the model version hasn’t changed, the immediate fix is horizontal pod or node scaling. Memory tip: “CPU crush, add nodes in a rush.”
PMLE Monitoring ML solutions Practice Question
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 travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is served using TensorFlow Serving on a Google Kubernetes Engine (GKE) cluster with auto-scaling enabled. The cluster uses n1-standard-4 machine types. The team has set up Cloud Monitoring dashboards and alerts. Last week, during a major holiday promotion, the team noticed that the model's inference latency P99 increased from 150 ms to 450 ms over a 30-minute period, while the request throughput increased from 500 to 1,200 requests per second. CPU utilization across the cluster rose to 95%, but memory utilization remained at 60%. The model version and the serving infrastructure configuration have not changed since the last deployment. Which action should the team take to mitigate the latency issue?
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
Add more nodes to the GKE cluster to increase the total CPU resources available for serving.
The latency spike is caused by CPU saturation (95% utilization) under increased load (500 to 1,200 RPS). Adding more nodes to the GKE cluster directly increases the total CPU resources available, allowing the existing TensorFlow Serving pods to handle the higher throughput without contention. This is the most immediate and infrastructure-appropriate fix because the model version and serving configuration have not changed, ruling out model-level or code-level optimizations.
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.
- ✗
Implement a feature engineering pipeline that compresses the input features to reduce data size and inference time.
Why it's wrong here
While potentially beneficial, this is a longer-term solution and does not provide immediate latency relief during the surge.
- ✗
Deploy a newer version of the model that uses a more efficient architecture to reduce computational complexity.
Why it's wrong here
Deploying a new model requires time for development, testing, and approval, and may not be feasible for immediate mitigation.
- ✗
Increase the number of TensorFlow Serving instances by reducing the CPU request per pod in GKE to allow more pods per node.
Why it's wrong here
Reducing CPU requests may lead to CPU starvation and pod instability, harming latency further.
- ✓
Add more nodes to the GKE cluster to increase the total CPU resources available for serving.
Why this is correct
Adding nodes increases compute capacity, allowing more parallel inference and reducing latency under high load.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that reducing per-pod CPU requests (Option C) is a valid scaling strategy, but in reality this increases overcommitment and can worsen latency under high load, whereas adding nodes (Option D) provides dedicated resources without contention.
Detailed technical explanation
How to think about this question
Under the hood, TensorFlow Serving uses gRPC or REST endpoints and each inference request consumes CPU cycles for model graph execution, batching, and serialization. When CPU utilization hits 95%, the Linux kernel's scheduler introduces context-switching delays and the kubelet may throttle pods if CPU limits are enforced, directly inflating P99 latency. In real-world scenarios, horizontal pod autoscaling (HPA) based on CPU may lag behind sudden traffic spikes, so proactive node-pool scaling or using cluster autoscaler with a higher minimum node count is often necessary.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
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: Add more nodes to the GKE cluster to increase the total CPU resources available for serving. — The latency spike is caused by CPU saturation (95% utilization) under increased load (500 to 1,200 RPS). Adding more nodes to the GKE cluster directly increases the total CPU resources available, allowing the existing TensorFlow Serving pods to handle the higher throughput without contention. This is the most immediate and infrastructure-appropriate fix because the model version and serving configuration have not changed, ruling out model-level or code-level optimizations.
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.
About these practice questions
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Last reviewed: Jun 30, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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