PCDOE Practice Test 33 — 15 Questions

Question 1

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 2

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. Option A is correct because 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.

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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 3

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

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Modify the application code to include profiling endpoints

Question 4

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

Question 5

A Cloud Run service experiences high latency during cold starts. The service is memory-intensive. Which configuration change will most effectively reduce cold start latency?

Accepted Answer

Set a minimum number of instances.. Setting a minimum number of instances ensures that a baseline of pre-warmed instances is always running, eliminating cold starts for the initial requests. For a memory-intensive service, this avoids the latency penalty of loading large libraries or datasets into memory on first invocation. This is the most direct and effective configuration change to reduce cold start latency.

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Decrease the container concurrency setting.

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Increase the max instances limit.

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Enable CPU boost for the service.

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.

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Disk read/write latency

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Network sent bytes

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CPU utilization

Question 7

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.

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Incorrect load balancer configuration

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Network policy restriction

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Sidecar proxy resource limits

Question 8

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.

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Rate limiting

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IP blacklist/whitelist

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Pre-configured WAF rules

Question 9

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.

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Cloud Logging

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Cloud Debugger

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Cloud Trace

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.. Option A is correct because 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.

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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.

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

Question 11

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 12

A DevOps team wants to serve static content from a Cloud Storage bucket with low latency globally. They also need TLS termination. Which load balancer type should they use?

Accepted Answer

External HTTP(S) Load Balancer. External HTTP(S) Load Balancer is the correct choice because it provides global anycast IP addresses, TLS termination at the Google Front End (GFE), and integrates directly with Cloud Storage buckets as a backend. This enables low-latency content delivery worldwide while offloading SSL decryption to the load balancer.

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External Network Load Balancer

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SSL Proxy Load Balancer

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Internal HTTP(S) Load Balancer

Question 13

Refer to the exhibit. What does the alert condition indicate?

Accepted Answer

It alerts for any Cloud Run revision that has more than 1000 requests in a 1-minute window.. The alert condition in the exhibit uses a per-revision metric (e.g., `run.googleapis.com/request_count`) with a threshold of 1000 and a 1-minute window. This means the alert fires for any individual Cloud Run revision that exceeds 1000 requests within that window, not for the aggregate across all revisions. Option B correctly identifies this per-revision behavior.

Answer

It alerts when the request count drops below 1000 for 1 minute.

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It alerts when the average request count across all revisions exceeds 1000 over 1 minute.

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It alerts when the total request count across all revisions exceeds 1000 per minute.

Question 14

A team is using Cloud Run for a containerized application. They notice that requests have high latency due to cold starts. Which configuration change would most effectively reduce cold start latency?

Accepted Answer

Set the min-instances parameter to a value > 0. Setting the `min-instances` parameter to a value greater than 0 keeps a baseline number of container instances always warm and ready to serve requests. This eliminates the cold start penalty because the runtime environment, including the container and its dependencies, is already initialized and listening for traffic. Cloud Run will scale down to this minimum count, ensuring that new requests are routed to pre-warmed instances rather than triggering a new container startup.

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Enable concurrent requests

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Increase the function timeout

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Reduce the container image size

Question 15

Which TWO practices should be implemented to optimize query performance in Cloud Spanner?

Accepted Answer

Use interleaved tables to co-locate related rows.. Option C is correct because interleaved tables in Cloud Spanner physically co-locate parent and child rows on the same split, reducing cross-node round trips and improving join performance. This design leverages Spanner's hierarchical storage model to minimize latency for queries that access related data together.

Answer

Split large tables into multiple smaller tables to distribute load.

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Create as many indexes as possible on all columns.

Answer

Use globally distributed interleaving across regions.