PCDOE Optimizing service performance Practice Test 2 — 15 Questions

Question 1

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?

Accepted Answer

Set a minimum number of instances to keep containers warm.. Setting a minimum number of instances (option B) ensures that a baseline of container instances is always warm and ready to serve requests, eliminating cold starts for those instances. Cold starts occur when a new container must be initialized, including loading large Python libraries, which adds significant latency. By keeping a minimum number of instances running, the service avoids the initialization delay for the first request to each instance.

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Increase the maximum number of concurrent requests per container.

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Set a longer request timeout.

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Increase the CPU allocation for the service.

Question 2

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?

Accepted Answer

Add more nodes to the instance and ensure read requests are distributed evenly.. In a read-heavy Spanner workload with high CPU utilization on some nodes, adding more nodes and ensuring read requests are distributed evenly (Option D) directly addresses the bottleneck by increasing the instance's compute capacity and spreading the load across all nodes. Spanner's architecture uses a shared-nothing design where each node handles a portion of the data and traffic; uneven distribution can cause hot spots. Adding nodes scales out processing power, and ensuring even distribution (e.g., via proper key design or using Spanner's built-in load balancing) reduces latency and CPU spikes on individual nodes.

Answer

Create read-only replicas in each region.

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Split the most frequently read tables into smaller tables.

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Increase the number of nodes in the instance.

Question 3

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?

Accepted Answer

The requests include unique query parameters like session IDs.. When Cloud CDN serves content with long Cache-Control headers but unique query parameters (like session IDs) are appended to each request, the cache treats each URL as a distinct object. This causes cache misses because the load balancer forwards requests with different query strings to the origin, preventing the CDN from serving cached responses. The correct answer is A because this behavior directly undermines cache efficiency despite proper cache headers.

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The Cache-Control max-age is set too short.

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The load balancer is configured with SSL termination.

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The content is served using signed URLs with expiration.

Question 4

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?

Accepted Answer

Examine App Engine request logs for latency patterns.. Option A is correct because examining App Engine request logs reveals latency patterns, such as which endpoints or operations are slow, helping to pinpoint whether the bottleneck is in the application code, network, or database. Option C is correct because enabling Cloud SQL slow query logging identifies long-running SQL queries that could be causing database contention or inefficient data retrieval, directly addressing a common performance bottleneck in App Engine applications using Cloud SQL.

Answer

Increase the number of App Engine instances.

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Enable Cloud CDN to cache responses.

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Disable caching to ensure fresh data.

Question 5

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?

Accepted Answer

Stripe data across multiple local SSD volumes using RAID 0.. Option B is correct because striping data across multiple local SSD volumes using RAID 0 increases the aggregate I/O throughput and IOPS by distributing read and write operations across all disks in parallel. This directly improves disk I/O performance without changing the instance type, as local SSDs are physically attached to the host and offer the highest performance when combined.

Answer

Migrate to persistent SSD for better durability.

Answer

Enable encryption for the local SSDs to reduce I/O overhead.

Question 6

Refer to the exhibit. An App Engine application returns 504 errors. The application calls an external API and processes the result. Which change is most likely to resolve the errors?

Accepted Answer

Increase the App Engine request timeout in app.yaml to 120 seconds.. A 504 error from App Engine indicates the request exceeded the timeout limit before the application could respond. The default App Engine request timeout is 60 seconds, and since the application calls an external API and processes the result, the total time may exceed this limit. Increasing the request timeout in app.yaml to 120 seconds allows the application more time to complete the external API call and processing, resolving the 504 error.

Answer

Reduce the idle timeout in the scaling settings.

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Change the scaling type from automatic to manual.

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Increase the number of instances to handle the load.

Question 7

Refer to the exhibit. A payment microservice on GKE logs frequent 'connection closed' errors. The service connects to a backend database. Which approach is most effective to reduce these errors?

Accepted Answer

Implement retry logic with exponential backoff in the service code.. The 'connection closed' errors indicate transient network failures or database server-side connection drops. Implementing retry logic with exponential backoff in the service code is the most effective approach because it allows the microservice to gracefully recover from intermittent failures without overwhelming the database with immediate retries. This pattern is a standard resilience technique for cloud-native applications on GKE, as it handles temporary issues like network blips or database connection pool exhaustion.

Answer

Increase the number of pod replicas to distribute load.

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Adjust the readiness probe to be more aggressive.

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Increase the CPU and memory limits for the container.

Question 8

Your team deploys a microservice on Google Kubernetes Engine (GKE) that serves an API with low latency requirements. Users report that the API occasionally times out during peak hours. You check the GKE metrics and see that CPU utilization is below 50% but memory is near 100% on the nodes. What is the most likely cause and what should you do?

Accepted Answer

The application is memory-constrained; increase memory resource limits for the pod.. Option B is correct because the symptoms—low CPU utilization but near 100% memory usage on nodes, with API timeouts during peak hours—indicate that the application is hitting memory limits. When a pod exceeds its memory request, the kernel can OOM-kill it, causing request failures and timeouts. Increasing the memory resource limits for the pod allows it to allocate more heap or cache, preventing OOM kills and stabilizing latency.

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The nodes are under-provisioned; add more nodes to the cluster.

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The application is CPU-bound; increase CPU resource limits for the pod.

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The network bandwidth is insufficient; increase the machine type for nodes.

Question 9

You created the above alert policy to detect high CPU utilization in your GKE cluster. However, you are receiving too many false positive alerts. What is the most likely reason?

Accepted Answer

The crossSeriesReducer is set to REDUCE_SUM, which sums CPU across containers, so a namespace with many containers can trigger the alert even if each container uses less than 80%.. Option B is correct because the crossSeriesReducer set to REDUCE_SUM aggregates CPU utilization across all containers in a namespace. This means that even if each container uses only 20% CPU, a namespace with five containers would show a total of 100%, triggering the alert when the threshold is 0.8 (80%). This causes false positives because the alert fires on the sum, not on individual container utilization.

Answer

The threshold value of 0.8 is too low; it should be 0.9 for production.

Answer

The duration of 300 seconds (5 minutes) is too short; it should be longer to avoid transient spikes.

Answer

The filter does not specify a specific namespace, causing alerts from all namespaces.

Question 10

Your company runs a web application on Compute Engine behind a global HTTP(S) Load Balancer. You want to improve performance for users in Europe. You have already enabled Cloud CDN. What is the next best action to reduce latency?

Accepted Answer

Add Compute Engine instances in a European region (e.g., europe-west1) and add them to the load balancer's backend.. Adding Compute Engine instances in a European region (e.g., europe-west1) and adding them to the load balancer's backend reduces latency by bringing the origin server physically closer to European users. Even with Cloud CDN enabled, cache misses still require a round trip to the backend; having a backend in Europe minimizes that distance. The global HTTP(S) Load Balancer automatically routes requests to the closest healthy backend, so this directly improves performance for users in Europe.

Answer

Switch to a regional load balancer to route traffic more efficiently.

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Configure a multi-regional Cloud CDN and enable cache warming for popular content.

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Increase the machine type of existing instances to improve processing speed.

Question 11

Your team is running a high-traffic web application on Google Kubernetes Engine (GKE) and has configured Horizontal Pod Autoscaling (HPA) based on CPU utilization. Recently, the application experienced intermittent latency spikes during traffic bursts. You suspect that the HPA is not scaling quickly enough. Which TWO actions would most effectively improve the autoscaling responsiveness?

Accepted Answer

Configure a custom metric in HPA based on the application's request latency (e.g., p99 latency).. Option B is correct because using a custom metric based on request latency (e.g., p99 latency) allows HPA to react to application performance degradation directly, rather than relying solely on CPU utilization which may lag behind traffic bursts. This provides a more immediate signal for scaling, as latency spikes often precede CPU saturation in web applications. Option D is correct because reducing the --horizontal-pod-autoscaler-upscale-stabilization window (default 5 minutes) to a lower value like 30 seconds decreases the time HPA waits before acting on scale-up recommendations, enabling faster response to sudden load increases.

Answer

Increase the target CPU utilization percentage to 90% so that pods are less likely to be added.

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Set the maxReplicas of the HPA to a value lower than the expected peak traffic to force faster scaling.

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Increase the --horizontal-pod-autoscaler-sync-period flag to 30 seconds and increase the --horizontal-pod-autoscaler-upscale-delay flag to 5 minutes.

Question 12

You are troubleshooting a performance issue with a Compute Engine instance that is part of a managed instance group serving a web application. Users report intermittent high latency. You run the command shown in the exhibit. Based on the output, what is the most likely cause of the performance issue?

Accepted Answer

The instance is under-provisioned for CPU.. The output shows high CPU utilization (e.g., 95%+), which directly correlates with the reported intermittent high latency. In a managed instance group, if the instance is under-provisioned for CPU, it cannot handle the workload spikes, causing queuing and increased response times. This is the most common cause of performance degradation in Compute Engine instances serving web applications.

Answer

The instance is hitting the network egress bandwidth limit.

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The service account lacks the necessary scopes for Cloud Monitoring and Cloud Trace.

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The boot disk is too small, causing I/O contention.

Question 13

You are a DevOps engineer at a media streaming company. Your application runs on Google Kubernetes Engine (GKE) and serves video content to users worldwide. The application uses a microservices architecture with a frontend service that handles user requests and a backend transcoding service that converts video files. Recently, you noticed that the transcoding service is causing performance bottlenecks during peak hours, leading to increased latency for users. You have enabled Cloud Monitoring and Cloud Trace and observed that the transcoding service's CPU utilization is consistently above 90% during peak times, and the queue of video transcoding tasks is growing. The current deployment has 5 replicas of the transcoding service with no autoscaling. You need to optimize the performance of the transcoding service to reduce latency. Your company has a limited budget and wants to minimize costs. What should you do?

Accepted Answer

Refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic, and create a separate deployment of workers that subscribe to the topic and perform transcoding. Configure HPA on the worker deployment based on the Pub/Sub subscription backlog.. Option D is correct because it decouples the transcoding workload from user-facing requests using Cloud Pub/Sub, allowing the worker deployment to scale independently based on the backlog of tasks. This pattern reduces latency by preventing the frontend from being blocked by transcoding, and HPA on Pub/Sub backlog ensures cost-efficient scaling only when demand increases, aligning with the limited budget.

Answer

Enable Horizontal Pod Autoscaling (HPA) on the transcoding service based on CPU utilization, targeting 70% utilization.

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Upgrade the transcoding service to a larger machine type with more CPU and memory.

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Increase the number of replicas of the transcoding service to 10 and keep it static.

Question 14

Arrange the steps to implement a canary deployment for a Cloud Run service.

Question 15

Match each monitoring concept to its purpose.