PCDOE Optimizing service performance Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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.

Answer

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

Answer

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

Question 4

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.. 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.

Answer

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 5

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

Answer

Errors: Demand placed on the system

Question 6

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 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

Answer

IP blacklist/whitelist

Answer

Pre-configured WAF rules

Question 8

A financial services company uses Spanner for their core database. They notice that some transactions are taking longer than expected, especially during cross-region writes. They have set up Spanner with regional configuration. What is the most likely cause?

Accepted Answer

The transaction is experiencing contention due to a hot spot. A is correct because cross-region writes in a regional Spanner configuration can lead to increased latency due to hot spotting. A hot spot occurs when many writes are concentrated on a single split (e.g., a monotonically increasing key), causing contention and serialization delays. This is especially pronounced in cross-region scenarios because Spanner's TrueTime and Paxos-based replication require consensus across zones, amplifying the impact of contention.

Answer

The transaction is using stale reads

Answer

The transaction is not using a read-write transaction

Answer

The transaction is too large

Question 9

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

Answer

Network policy restriction

Answer

Sidecar proxy resource limits

Question 10

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.

Answer

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.

Question 11

An application on GKE frequently reads the same data from a Cloud Storage bucket. The data changes rarely. Which solution will best improve read performance and reduce costs?

Accepted Answer

Deploy a sidecar container that caches the data in an emptyDir volume.. Deploying a sidecar container with an emptyDir volume caches frequently accessed, rarely changing data locally within the pod. This reduces latency by avoiding repeated network calls to Cloud Storage and eliminates egress and operation costs for those reads. emptyDir volumes are ephemeral and tied to the pod's lifecycle, making them ideal for caching read-heavy, static data without incurring persistent storage costs.

Answer

Configure a Cloud SQL read replica for the data.

Answer

Increase the number of nodes in the cluster.

Answer

Use a StatefulSet with a persistent volume claim to store the data.

Question 12

Refer to the exhibit. After applying the shown firewall rule, users report increased latency to a web application. What is the most likely cause?

Accepted Answer

The rule contains both allow and deny for the same traffic, creating a conflict.. In Google Cloud VPC firewall rules, a single rule cannot specify both allow and deny actions. The rule shown is invalid because it combines both actions for the same traffic, which can lead to undefined behavior. This misconfiguration can cause the firewall to improperly deny traffic that should be allowed, resulting in connectivity issues and increased latency due to retries or timeouts. Option B correctly identifies this conflict. Option A is wrong because priority 1000 is not low (lower number higher priority); logging alone does not cause significant latency. Option C is wrong because a broad source range does not inherently cause latency. Option D is wrong because the rule's priority is not the issue here.

Answer

The rule priority is set to 1000, which is too low.

Answer

The source range covers all IPs, causing excessive traffic.

Answer

The firewall rule has logging enabled, which adds overhead.

Question 13

A team wants to optimize a batch processing job that is CPU-bound. Which Compute Engine machine family should they use?

Accepted Answer

C2. C2 is the correct machine family because it is specifically designed for compute-intensive, CPU-bound workloads. It offers the highest clock speed and per-core performance among Compute Engine machine families, making it ideal for batch processing jobs that are limited by CPU throughput rather than memory or I/O.

Answer

E2

Answer

N2

Answer

M2

Question 14

A team is optimizing a Cloud Run service. Which two actions can reduce request latency? (Select TWO.)

Accepted Answer

Reduce container image size. Option C is correct because reducing container image size shortens the time Cloud Run needs to pull and unpack the image before starting a new instance, which directly lowers cold-start latency and thus request latency. Option E is correct because enabling min-instances keeps a specified number of warm instances running at all times, eliminating cold starts for requests served by those instances and thereby reducing request latency. Option A (increase max-instances) only raises the ceiling on concurrent instance count for scaling under load; it does not by itself make any individual request faster. Option B (enable HTTP/2) can improve multiplexing and efficiency for clients that support it, but it is not one of the marked correct actions for reducing latency in this scenario. Option D (use a regional endpoint) affects routing/endpoint selection rather than the per-request execution latency of the Cloud Run service.

Answer

Increase max-instances

Answer

Enable HTTP/2

Answer

Use a regional endpoint

Question 15

A DevOps team wants to monitor the performance of a Cloud SQL database. Which two metrics should they track? (Select TWO.)

Accepted Answer

CPU utilization. Option C (CPU utilization) is correct because Cloud SQL exposes CPU utilization as a core performance metric that indicates how heavily the database instance's compute resources are being used, helping the team detect saturation and decide when to scale up or optimize queries. Option E (Disk read/write latency) is correct because disk I/O latency directly reflects storage performance and is a key Cloud SQL metric for identifying slow reads/writes, storage bottlenecks, or issues with the underlying persistent disk that degrade query response times. Option A (Auto-increment counter) is not a Cloud SQL performance metric; it is a schema/application-level value tracking the next sequence number, not a monitored performance indicator. Option B (Query error rate) is not a standard Cloud SQL performance metric — errors are typically tracked via logs or application-level monitoring rather than as a built-in performance metric. Option D (Number of active connections) is a useful operational metric, but it is not one of the two performance metrics intended here, which focus on resource utilization and storage latency.

Answer

Auto-increment counter

Answer

Query error rate

Answer

Number of active connections