PCDOE Optimizing service performance Practice Test 3 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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.. 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.

Answer

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

Question 2

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.

Answer

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.

Answer

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

Question 3

Match each monitoring concept to its purpose.

Accepted Answer

Latency: Time taken to serve a request. The four golden signals are Latency, Traffic, Errors, and Saturation. Correct matches are A, B, C, D. Common confusions swap Error and Traffic definitions.

Answer

Latency: Rate of failed requests

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Errors: Demand placed on the system

Question 4

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.

Answer

Cloud Monitoring

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Cloud Profiler

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Cloud Logging

Question 5

A company is using Cloud CDN to deliver static content globally. Some users in Asia report slow load times. Which configuration change would most likely improve performance for these users?

Accepted Answer

Use a global external HTTP(S) load balancer with Cloud CDN. Cloud CDN relies on a global external HTTP(S) load balancer to route user requests to the nearest cache node. Without this load balancer, Cloud CDN cannot leverage Google's global network and edge caches, so users in Asia would not be served from a nearby point of presence. Option C correctly identifies that using a global external HTTP(S) load balancer with Cloud CDN enables geographic load balancing and edge caching, directly improving latency for distant users.

Answer

Add an additional CDN origin

Answer

Enable Cloud Armor

Answer

Increase the cache TTL

Question 6

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.

Answer

Disk read/write latency

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Network sent bytes

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CPU utilization

Question 7

An organization uses Cloud Armor to protect their web application. After enabling the service, they notice increased latency on some requests. Which Cloud Armor feature is most likely causing this?

Accepted Answer

Geo-based access control. Geo-based access control (D) is the most likely cause of increased latency because it requires Cloud Armor to perform a GeoIP lookup on every request to determine the geographic origin. This lookup adds processing overhead, especially if the organization has a large or complex set of geo-based rules, which can introduce measurable delay.

Answer

Rate limiting

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IP blacklist/whitelist

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Pre-configured WAF rules

Question 8

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

Answer

Use Cloud Functions

Question 9

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.

Answer

Increase the machine type

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Use a private pool

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Enable parallel builds

Question 10

A company runs a microservices architecture on GKE with Istio service mesh. They observe that service-to-service latency has increased after enabling mTLS. What is the most likely cause?

Accepted Answer

mTLS encryption overhead. Enabling mTLS in Istio encrypts all service-to-service traffic using mutual TLS, which adds CPU overhead for encryption and decryption of each request. This encryption overhead directly increases latency, especially for high-throughput or small-payload services, as the sidecar proxies must perform TLS handshakes and cryptographic operations on every packet.

Answer

Incorrect load balancer configuration

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Network policy restriction

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Sidecar proxy resource limits

Question 11

A team is optimizing the performance of their application running on Cloud Run. They want to reduce cold starts. Which two actions would help? (Select TWO)

Accepted Answer

Enable min instances. Enabling min instances (option A) keeps a baseline number of container instances always warm and ready to serve requests, eliminating the cold start latency for those instances. This directly reduces the time required to spin up a new container when traffic spikes, as the pre-warmed instances can handle requests immediately.

Answer

Increase the maximum number of container instances

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Increase the CPU limit

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Enable HTTP/2

Question 12

A company uses Cloud Monitoring to set up alerting for their production system. They want to reduce alert fatigue while ensuring critical issues are caught quickly. Which two strategies should they implement? (Select TWO)

Accepted Answer

Use notification channels with escalation policies. Notification channels with escalation policies ensure that alerts are routed to the appropriate responders based on severity and time thresholds, reducing noise by preventing low-severity issues from repeatedly notifying the same person. Escalation policies automatically escalate unacknowledged critical alerts to higher-level teams, ensuring critical issues are caught quickly without overwhelming on-call staff.

Answer

Use low threshold values to catch issues early

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Disable alerts during off-hours

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Set up separate alerts for each microservice

Question 13

A DevOps team is investigating performance issues in their GKE cluster. They want to use Cloud Profiler to identify the bottleneck. Which three steps are required to start profiling? (Select THREE)

Accepted Answer

Configure IAM permissions. A is correct because Cloud Profiler requires the `cloudprofiler.agent` IAM role (or equivalent permissions) on the service account used by the GKE node or application to allow the agent to write profiling data to the Cloud Profiler API. Without this permission, the agent cannot upload profiles, and no data will appear in the console.

Answer

Install a sidecar proxy

Answer

Modify the application code to include profiling endpoints

Question 14

A company notices increased latency for their web application running on Compute Engine. They suspect a database bottleneck. Which Google Cloud service should they use to identify slow queries?

Accepted Answer

Cloud SQL Query Insights. Cloud SQL Query Insights is the correct choice because it is a Google Cloud managed service specifically designed to identify and analyze database performance issues, including slow queries, in Cloud SQL instances. It provides detailed query performance metrics, execution plans, and recommendations to optimize database queries, directly addressing the bottleneck in a Compute Engine web application.

Answer

Cloud Logging

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Cloud Debugger

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Cloud Trace

Question 15

A DevOps team wants to autoscale a GKE Deployment based on a custom metric exposed by the application. The metric is available via an HTTP endpoint. Which approach should they use to integrate this metric with the Horizontal Pod Autoscaler (HPA)?

Accepted Answer

Deploy a Prometheus Operator with the kube-state-metrics adapter and configure the HPA to use the custom metric.. The Prometheus Operator, combined with the kube-state-metrics adapter (or the prometheus-adapter), allows HPA to consume custom metrics from a Prometheus server that scrapes the application's HTTP endpoint. The adapter exposes these metrics via the custom.metrics.k8s.io API, which HPA natively queries. This is the standard approach for integrating application-specific HTTP metrics into Kubernetes autoscaling.

Answer

Expose the metric via an Ingress and configure HPA to read from the Ingress metrics.

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Use the standard CPU-based HPA and map the custom metric to CPU usage via a script.

Answer

Configure the Stackdriver Metrics Adapter to collect the metric from the endpoint.