PCDOE Optimizing service performance Practice Test 2 — 15 Questions

Question 1

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

Accepted Answer

Optimize data transfer by compressing responses.. Compressing responses reduces the amount of data transferred over the network, which directly lowers egress costs charged by cloud providers. Smaller payloads also reduce latency and improve perceived performance for end users, as less data needs to travel across regions.

Answer

Increase the number of instances in each region.

Answer

Use premium tier networking for lower latency.

Question 2

Your company runs a multi-region e-commerce platform on Google Kubernetes Engine (GKE) with services in us-central1 and europe-west1. The application uses a global external HTTP(S) load balancer with Cloud CDN for static assets. Recently, users in Asia report that product images take 5-10 seconds to load, while users in the US and Europe experience sub-second load times. You check the Cloud CDN cache hit ratio and see it is 95% globally. You also notice that the images are served from a backend bucket in us-central1. The load balancer uses the default routing configuration. Your team has implemented client-side caching with Cache-Control headers set to public, max-age=3600. What is the most likely cause of the high latency for Asian users?

Accepted Answer

The cache hit ratio is too low for Asian users due to different traffic patterns.. While Cloud CDN has edge caches in Asia (e.g., Tokyo, Mumbai, Singapore), the global cache hit ratio of 95% does not guarantee a high hit ratio for Asian users. If their traffic patterns differ (e.g., first requests for new products), they may experience cache misses, forcing requests to the backend bucket in us-central1, resulting in high latency. Options A and B are not likely: the load balancer uses premium tier by default, and max-age=3600 is reasonable. Option C is incorrect because Cloud CDN does have edge caches in Asia.

Answer

The load balancer is not using premium tier networking, so traffic from Asia takes a longer path.

Answer

Client-side caching with max-age=3600 is too short, causing frequent revalidation.

Answer

Cloud CDN does not have edge caches in Asia, so requests are served from the nearest available edge location, which may be far from users.

Question 3

A team deploys a microservice on Google Kubernetes Engine (GKE) that processes user uploads. The service latency has increased over time. Monitoring shows that CPU utilization is low, but memory usage is high and garbage collection (GC) pauses are frequent. Which action is most likely to reduce latency?

Accepted Answer

Increase the memory limit and requests for the container.. Frequent GC pauses and high memory usage with low CPU indicate the JVM heap is too small, causing the garbage collector to run more often. Increasing the memory limit and requests gives the JVM more headroom to reduce GC frequency, directly lowering latency. This is a classic JVM tuning scenario in containerized environments like GKE.

Answer

Scale out the deployment by increasing the number of replicas.

Answer

Reduce the number of replicas to concentrate load.

Answer

Increase the CPU limit to allow faster processing.

Question 4

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?

Accepted Answer

Analyze Cloud Monitoring metrics for the load balancer, including backend latency and request counts.. Cloud Monitoring provides detailed metrics for TCP/UDP Network Load Balancers, including backend latency and request counts, which directly help identify whether the latency originates from the load balancer itself or the backend instances. Since instance CPU is below 50% and application logs show no errors, the issue is likely at the network or load balancer level, and these metrics offer the most targeted diagnostic data without changing the architecture.

Answer

Increase the number of instances behind the load balancer.

Answer

Enable VPC Flow Logs and analyze for dropped packets.

Answer

Switch to an HTTP(S) Load Balancer for better visibility.

Question 5

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.

Answer

Increase the maximum number of concurrent requests per container.

Answer

Set a longer request timeout.

Answer

Increase the CPU allocation for the service.

Question 6

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.

Answer

Split the most frequently read tables into smaller tables.

Answer

Increase the number of nodes in the instance.

Question 7

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.

Answer

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

Answer

The load balancer is configured with SSL termination.

Answer

The content is served using signed URLs with expiration.

Question 8

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.. 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.

Answer

Enable Cloud CDN to cache responses.

Answer

Disable caching to ensure fresh data.

Question 9

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.. 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 10

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.

Answer

Change the scaling type from automatic to manual.

Answer

Increase the number of instances to handle the load.

Question 11

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.

Answer

Adjust the readiness probe to be more aggressive.

Answer

Increase the CPU and memory limits for the container.

Question 12

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.. 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.

Answer

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

Answer

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

Answer

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

Question 13

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%.. 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 14

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.

Answer

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

Answer

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

Question 15

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).. 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.

Answer

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

Answer

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