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Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

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Optimizing service performance practice questions

Practise Google Professional Cloud DevOps Engineer Optimizing service performance practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Optimizing service performance

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What the exam tests

What to know about Optimizing service performance

Optimizing service performance questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Optimizing service performance exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Optimizing service performance questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Optimizing service performance explanation →

Your team has deployed a microservices application on Google Kubernetes Engine (GKE). You notice that one service has high latency during peak hours. The service is CPU-bound and uses a HorizontalPodAutoscaler (HPA) based on CPU utilization. What is the most likely cause of the latency?

Trap 1: The GKE cluster uses preemptible nodes that are frequently…

Preemptible nodes cause pod evictions, not gradual latency increase.

Trap 2: The service uses a global external HTTP(S) load balancer with…

Session affinity does not cause latency; it routes requests to the same backend.

Trap 3: The application does not implement request autoscaling at the…

Request autoscaling is not a built-in GKE concept.

Study all Optimizing service performance common traps →

A
The GKE cluster uses preemptible nodes that are frequently reclaimed.
Why wrong: Preemptible nodes cause pod evictions, not gradual latency increase.
B
The HPA's target CPU utilization is set too high, causing the autoscaler to react slowly.
A high target CPU threshold delays scaling, leading to latency.
C
The service uses a global external HTTP(S) load balancer with session affinity.
Why wrong: Session affinity does not cause latency; it routes requests to the same backend.
D
The application does not implement request autoscaling at the application layer.
Why wrong: Request autoscaling is not a built-in GKE concept.

Optimizing service performance practice questions

What to know about Optimizing service performance

Common Optimizing service performance exam traps

Optimizing service performance questions

Your team has deployed a microservices application on Google Kubernetes Engine (GKE). You notice that one service has high latency during peak hours. The service is CPU-bound and uses a HorizontalPodAutoscaler (HPA) based on CPU utilization. What is the most likely cause of the latency?

A Cloud Run service is experiencing increased cold start latency. The service is written in Python and uses several large dependencies. Which action would most effectively reduce cold start latency?

You are designing a globally distributed application using Cloud Spanner. The application has a write-heavy workload. You notice that write latency increases as the number of nodes increases. What is the most likely cause?

A company runs a stateful workload on Compute Engine VMs with persistent disks. They observe that disk I/O latency spikes periodically. The workload is sensitive to latency. What should they do to improve performance?

Your GKE cluster runs a batch job that processes large files from Cloud Storage. The job uses CPUs inefficiently, with low utilization. You want to reduce cost while maintaining throughput. Which approach should you take?

You are using Cloud CDN with an external HTTPS load balancer. Users in Asia report slow load times for static assets. The origin is in us-central1. What should you do to improve performance?

Your application uses Cloud SQL for MySQL and you notice that read replica lag is increasing. Which action would most likely reduce replica lag?

You are using Memorystore for Redis as a cache for a high-traffic web application. You observe that cache hit ratio is low, causing high database load. What is the most effective way to improve cache hit ratio?

Which TWO actions can reduce tail latency in a microservices architecture deployed on GKE? (Choose 2)

Which THREE factors should you consider when designing a Cloud Run service for optimal performance under unpredictable traffic patterns? (Choose 3)

Which TWO metrics from Cloud Monitoring would best indicate that a GKE workload is experiencing CPU throttling due to a resource quota? (Choose 2)

Which THREE approaches can help reduce egress costs while improving performance for a multi-region application using Cloud Load Balancing? (Choose 3)

A company runs a critical application on Compute Engine instances behind a TCP/UDP Network Load Balancer. They notice intermittent high latency for a subset of users. The application logs show no errors, and instance CPU is below 50%. Which next step is most effective to diagnose the latency?

A DevOps engineer is optimizing a Cloud Run service that experiences cold starts. The service is written in Python and uses several large libraries. Which change is most effective to reduce cold start latency?

A team uses Spanner for a global database. They notice increased read latency and high CPU utilization on some nodes. The workload is read-heavy with occasional writes. Which action is most likely to improve performance?

An organization uses Cloud CDN with an HTTP(S) Load Balancer to serve static content. They observe that cache hit ratio is lower than expected. The content is immutable and has long Cache-Control headers. What is the most likely cause?

A team is troubleshooting a slow response time on an App Engine standard environment application. The application uses Cloud SQL as its database. Which TWO actions should the team take to identify the bottleneck?

A company runs a stateful workload on Compute Engine with local SSDs. They need to improve disk I/O performance without changing the instance type. Which THREE actions should they take?

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