PCDOE Optimizing service performance Practice Test 5 — 15 Questions

Question 1

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?

Accepted Answer

Set a minimum number of instances to keep containers warm.. Setting a minimum number of instances ensures that the Cloud Run service always has a pool of warm containers ready to serve requests, eliminating the cold start penalty. Cold starts in Python are particularly severe due to the time required to import large dependencies (e.g., NumPy, TensorFlow) and initialize the runtime. By keeping containers alive, you bypass the entire initialization phase, directly addressing the root cause of increased latency.

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Set concurrency to 1 to ensure each request gets a dedicated container.

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Increase the CPU allocation to 4 vCPUs.

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Increase memory to 2 GiB.

Question 2

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

Accepted Answer

Increase the machine type of the replica.. Increasing the machine type of the replica (Option C) directly addresses read replica lag by providing more CPU and memory resources to the replica instance. This allows the replica to apply binary log (binlog) events from the primary faster, reducing the replication lag. Cloud SQL for MySQL uses asynchronous replication, so a replica with insufficient resources cannot keep up with the write throughput of the primary.

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Configure automatic failover to the replica.

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Decrease the memory of the primary instance.

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Promote the replica to a standalone instance.

Question 3

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

Accepted Answer

Run multiple replicas of each service and use a load balancer with least-request algorithm.. Option A is correct because running multiple replicas of each service removes single-pod queuing bottlenecks, and a load balancer using the least-request algorithm routes new requests to the replica with the fewest in-flight requests, which directly reduces the chance that a request lands behind a long queue and experiences tail latency. Option E is correct because request hedging sends duplicate requests to multiple replicas and returns the first successful response, so a slow or straggling replica no longer determines the tail latency for that request; this is a standard technique for cutting p99 latency at the cost of extra load. Option B is not correct because a single large machine type for all services does not address per-request queuing or straggler replicas and can create noisy-neighbor and scaling problems. Option C is not correct because session affinity pins users to the same pod, which can concentrate load and worsen tail latency rather than reduce it. Option D is not correct because increasing batch size generally increases per-request processing time and can raise tail latency, not lower it.

Answer

Use a single large machine type for all services.

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Enable session affinity to keep users on the same pod.

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Increase the batch size for processing requests.

Question 4

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.

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Scale out the deployment by increasing the number of replicas.

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Reduce the number of replicas to concentrate load.

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Increase the CPU limit to allow faster processing.

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.

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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 6

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.

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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 7

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.

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Enable Cloud CDN to cache responses.

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Disable caching to ensure fresh data.

Question 8

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.

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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 9

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.

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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 10

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

Accepted Answer

Deploy new revision with 0% traffic, then shift a small percentage of traffic to it, then monitor, then gradually increase traffic, then remove old revision.. In a canary deployment for Cloud Run, the correct sequence ensures minimal risk. First, deploy the new revision with 0% traffic. Then, route a small percentage (e.g., 1%) to it and monitor for errors or performance issues. If successful, gradually increase traffic while monitoring. Once the new revision is fully validated, point 100% traffic to it and remove the old revision. This incremental approach limits blast radius and allows rollback.

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Shift a small percentage of traffic to the new revision, then deploy the new revision, then monitor, then gradually increase traffic, then remove old revision.

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Deploy new revision with 0% traffic, then gradually increase traffic to 100%, then monitor, then remove old revision.

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Deploy new revision with 0% traffic, then shift a small percentage of traffic, then remove old revision, then monitor, then gradually increase traffic.

Question 11

A DevOps team is troubleshooting a web application that shows high latency during peak hours. The application runs on Google Kubernetes Engine (GKE). They want to identify which specific API calls are causing the delay. Which Google Cloud tool should they use?

Accepted Answer

Cloud Trace. Cloud Trace is the correct tool because it is a distributed tracing system that captures latency data from applications, allowing you to trace individual API calls and identify which specific endpoints are causing delays. Unlike Cloud Monitoring, which provides aggregate metrics, Cloud Trace provides per-request latency breakdowns, making it ideal for pinpointing slow API calls in a GKE environment.

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Cloud Monitoring

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Cloud Profiler

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Cloud Logging

Question 12

An e-commerce platform uses Cloud SQL for its database. The team notices that read queries are slow. They want to improve read performance without significant cost increase. Which action should they take?

Accepted Answer

Add a read replica. Adding a read replica is the most cost-effective way to improve read query performance in Cloud SQL. A read replica offloads SELECT queries from the primary instance, reducing contention and latency for read-heavy workloads, without requiring a more expensive primary instance. This approach scales read capacity horizontally while keeping the primary instance's compute and storage unchanged.

Answer

Increase the number of vCPUs on the instance

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Increase the storage size

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Enable binary logging

Question 13

A team is running a stateful application on Compute Engine VMs. They notice that the application performance degrades over time as the disk fills up. They want to proactively alert before performance degrades. Which metric should they monitor?

Accepted Answer

Disk usage percentage. Disk usage percentage is the correct metric because the application performance degrades as the disk fills up, which is a capacity issue. Monitoring disk usage percentage allows the team to set an alert threshold (e.g., 80% or 90%) to proactively take action (e.g., clean up logs or resize disks) before the disk becomes full and causes performance degradation. This directly addresses the root cause described in the scenario.

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Disk read/write latency

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Network sent bytes

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CPU utilization

Question 14

A gaming company runs a real-time multiplayer server on GKE. They want to minimize latency between players worldwide. Which approach should they use?

Accepted Answer

Use a multi-cluster setup with clusters in multiple regions and use a global load balancer. A multi-cluster setup with clusters in multiple regions, fronted by a global load balancer, minimizes latency by placing game servers physically closer to players worldwide. The global load balancer uses Anycast IP and Google Front Ends (GFEs) to route traffic to the nearest healthy backend cluster, reducing round-trip time (RTT). This approach is specifically designed for real-time multiplayer workloads that require low latency across geographic regions.

Answer

Increase the number of nodes in the cluster

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Use a single regional cluster

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Use Cloud Functions

Question 15

A DevOps team is using Cloud Build to build and push container images. The build times have increased significantly. They suspect that the build cache is not being used effectively. Which build configuration change would likely improve cache usage?

Accepted Answer

Use kaniko instead of Docker. Kaniko is a cache-aware container image builder that can leverage a remote image registry as a cache layer, unlike the default Docker builder which relies on a local Docker daemon and its local layer cache. By using Kaniko with a configured cache repository, the team can reuse previously built layers across builds, even when builds run on different Cloud Build workers, significantly reducing build times.

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Increase the machine type

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Use a private pool

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Enable parallel builds