PCDOE Practice Test 14 — 15 Questions

Question 1

A platform team operates a GKE cluster with Cloud Service Mesh enabled. They want alerting policies to fire when the 99th percentile latency of the frontend service breaches 800 ms for five continuous minutes, and they want the alert to include the affected service name. Which configuration should they use?

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

Accepted Answer

A metric-threshold alerting policy on the service latency distribution metric, using an alignment period and a percentile aligner with a five-minute duration condition.. Percentile alerting on a distribution metric requires aligning the distribution with a percentile aligner to produce the p99 series, then applying a threshold condition sustained over the required duration. Cloud Monitoring metric-threshold policies support duration conditions and propagate metric labels, so the service name appears in the incident, giving responders immediate context.

Answer

A Prometheus alerting rule evaluated by the Prometheus server that scrapes the mesh sidecars, routed through Alertmanager.

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A log-based alerting policy that matches frontend access log entries whose latency field exceeds 800 ms.

Answer

An uptime check with a response-time threshold of 800 ms and a five-minute check interval.

Question 2

Your team runs a Java-based microservice on Google Kubernetes Engine (GKE). The service uses a Java Virtual Machine (JVM) heap of 4 GiB and is configured with a container memory limit of 5 GiB. During load testing, the pods are frequently OOMKilled. The JVM flags do not include any container awareness settings. What is the most likely cause of the OOMKilled events?

Accepted Answer

The JVM is not container-aware and sizes its heap based on the node's total memory, causing it to exceed the container limit.. The JVM, without container awareness, often allocates heap based on the host's total memory, not the cgroup limit. This can cause the JVM to exceed the container's memory limit, leading to OOMKilled. Setting explicit JVM flags like -XX:MaxRAMPercentage or -Xmx to a value below the limit ensures the JVM respects the container constraints. The other options do not directly cause OOMKilled in this context.

Answer

The GKE node has insufficient memory, causing the kernel to kill the container to free up resources.

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The Kubernetes liveness probe is misconfigured, causing the kubelet to restart the pods and report OOMKilled.

Answer

The container memory limit is set too low because the JVM heap is 4 GiB, leaving only 1 GiB for non-heap memory, which is insufficient.

Question 3

Which THREE actions can help optimize Cloud Storage costs? (Choose three.)

Accepted Answer

Use Nearline or Coldline storage classes for infrequently accessed data.. Nearline and Coldline storage classes are designed for infrequently accessed data, offering lower storage costs compared to Standard storage. By choosing these classes for data that is accessed less than once a quarter or once a year, you reduce the per-GB storage charge, though you incur higher retrieval and early deletion fees. This directly optimizes costs when access patterns align with the class's intended use.

Answer

Enable versioning on all buckets to protect against accidental deletion.

Answer

Use Standard storage class for all objects to ensure low latency.

Question 4

A DevOps team manages a high-traffic API on GKE. They use Cloud Monitoring to track performance. They want to detect subtle latency regressions after each deployment and automatically roll back if the 99th percentile latency exceeds a threshold. Which approach should they use?

Accepted Answer

Use Cloud Deploy with a canary deployment strategy and configure an analysis job that queries Cloud Monitoring for latency metrics.. Cloud Deploy provides canary deployment strategies with automated analysis. By defining an analysis job that queries Cloud Monitoring for the 99th percentile latency, Cloud Deploy can automatically compare the canary's performance against the baseline and trigger a rollback if the threshold is exceeded. This is a fully managed, integrated solution that requires minimal custom code. The other options either require manual intervention, lack automation, or do not provide rollback capabilities.

Answer

Create a Cloud Monitoring alerting policy on the 99th percentile latency metric and trigger a Cloud Function to roll back the deployment.

Answer

Implement a custom CI/CD pipeline that uses Prometheus to scrape latency metrics and triggers a rollback via kubectl.

Answer

Configure a GKE Horizontal Pod Autoscaler based on the 99th percentile latency metric.

Question 5

Your team runs a microservices application on GKE. You want to reduce inter-service latency by ensuring that traffic between services stays within the same zone and avoids unnecessary hops. Which GKE feature should you configure?

Accepted Answer

Topology-aware routing for Services.. Topology-aware routing is a GKE feature that directs traffic to endpoints in the same zone as the client, minimizing latency and cross-zone costs. It is configured per Service and is the correct choice for keeping inter-service traffic local. Network policies, Cloud CDN, and Istio ingress gateway serve different purposes and do not address zone-local routing for internal services.

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Cloud CDN to cache responses between services.

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Istio ingress gateway to manage incoming traffic.

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Network policies to restrict traffic between namespaces.

Question 6

A team uses Spinnaker on GKE for deployment. They notice that deployments are taking too long because of manual judgment gates. They want to automatically approve deployments if the canary analysis passes predefined thresholds. What Spinnaker feature should they use?

Accepted Answer

Automated canary analysis. Spinnaker's Automated Canary Analysis (ACA) feature allows teams to define canary analysis thresholds and automatically promote or roll back deployments based on the analysis results, eliminating the need for manual judgment gates. This directly addresses the requirement to automatically approve deployments when predefined thresholds are met, without human intervention.

Answer

Automated rollback

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Policy engine with OPA

Answer

Pipeline expressions

Question 7

A media company serves video thumbnails from a Cloud Storage bucket through an external Application Load Balancer with Cloud CDN enabled. Analytics show a cache hit ratio near 20 percent for image objects, even though the same images are requested repeatedly by millions of users. Response headers show Cache-Control: private, max-age=600 on the objects. You need to raise the hit ratio without changing application code. What should you do?

Accepted Answer

Change the object metadata to Cache-Control: public, max-age=86400 and invalidate the cached entries.. Cloud CDN honors origin Cache-Control semantics, and a private directive explicitly forbids shared caches from storing the response. Thumbnails are identical for all users, so they should be publicly cacheable with a long max-age. Updating object metadata and invalidating the previously stored entries makes the objects eligible for edge caching and immediately reflects the new policy, lifting the hit ratio without touching application code.

Answer

Increase the backend bucket's capacity and set a longer default TTL in the load balancer's backend configuration.

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Move the thumbnails into a regional bucket in the same region as the load balancer to reduce origin latency.

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Enable negative caching on the backend bucket so misses are remembered and revalidated less often.

Question 8

A company uses Cloud Build and wants to trigger builds only from the master branch. Which configuration is required?

Accepted Answer

Set the branch filter to 'master' in the trigger.. Cloud Build triggers allow you to specify a branch filter using a regex pattern. Setting the filter to 'master' ensures that only pushes or pull requests targeting the master branch initiate the build. This is the native, supported method for branch-based triggering without additional overhead.

Answer

Create separate triggers for each branch.

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Use a custom Cloud Build step to check the branch name.

Answer

Use a Cloud Function to call Cloud Build for master only.

Question 9

Which THREE of the following are recommended practices for writing effective post-mortem documents?

Accepted Answer

Focus on the root cause and contributing factors.. Option A is correct because an effective post-mortem must identify the root cause and the contributing factors that allowed the incident to occur, so the team can address the underlying systemic issues rather than just symptoms. Option C is correct because sharing findings across the entire organization turns a single team's incident into organizational learning, helping other teams recognize and prevent similar failure modes. Option D is correct because a post-mortem should produce a concrete action plan with owners and deadlines to prevent recurrence, otherwise the analysis yields no lasting improvement. Options B and E are not recommended: blaming an individual undermines the blameless culture that makes post-mortems honest and effective, and deliberately keeping the document brief to save time sacrifices the detail and rigor needed to fully understand and prevent the incident.

Answer

Assign blame to the responsible engineer.

Answer

Keep the document brief to save time.

Question 10

Your team runs a microservices application on Google Kubernetes Engine (GKE). You notice that some services experience increased latency due to frequent garbage collection pauses in the Java Virtual Machine (JVM). You want to optimize JVM performance in the containers. Which two actions should you take? (Choose two.)

Accepted Answer

Set the JVM's initial and maximum heap sizes to the same value to avoid heap resizing overhead.. To reduce GC pauses, setting -Xms and -Xmx to the same value avoids heap resizing overhead, and using G1GC with a tuned pause time target helps achieve low latency. Increasing memory limit alone may not help without JVM tuning, disabling JIT is harmful, and Serial GC is unsuitable for larger heaps. These two actions directly address the garbage collection pauses.

Answer

Set the JVM's garbage collector to Serial GC to minimize overhead.

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Disable the JVM's Just-In-Time (JIT) compiler to reduce CPU usage.

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Increase the container memory limit to allow the JVM to use more heap space.

Question 11

A DevSecOps team is configuring Cloud Monitoring alerts for proactive incident response. Which two practices are recommended for effective alerting? (Choose two.)

Accepted Answer

Define clear escalation paths for different alert severities.. Option A is correct because defining clear escalation paths for different alert severities ensures that the right responders are engaged at the right time, preventing critical incidents from being missed and reducing mean time to resolution (MTTR). Option E is correct because creating separate alerts for different symptom classes (e.g., latency, error rate, saturation) allows responders to quickly identify the specific failure domain and apply targeted remediation, rather than being overwhelmed by a monolithic alert. Option B is incorrect because alerting on every microsecond of latency increase generates excessive noise and alert fatigue, which degrades incident response. Option C is incorrect because a single high-level alert covering all symptoms lacks diagnostic granularity and delays root-cause identification. Option D is incorrect because setting thresholds based on arbitrary guesses rather than historical baselines or SLOs leads to false positives or missed detections.

Answer

Alert on every microsecond of latency increase.

Answer

Use a single high-level alert that covers all symptoms.

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Set alert thresholds based on arbitrary guesses.

Question 12

Which service provides built-in dashboards for Google Cloud services?

Accepted Answer

Cloud Monitoring. Cloud Monitoring (option D) provides built-in dashboards for Google Cloud services, offering pre-configured visualizations of metrics, logs, and alerts without requiring manual setup. These dashboards aggregate data from services like Compute Engine, Cloud SQL, and Kubernetes Engine, enabling real-time monitoring of resource utilization and performance. This aligns with the PCDOE domain of implementing service monitoring strategies by providing out-of-the-box observability.

Answer

Cloud Shell

Answer

Cloud Console

Answer

Cloud Logging

Question 13

You are designing SLO monitoring for a high-traffic e-commerce platform. Which three best practices should you follow? (Choose three.)

Accepted Answer

Use multiple SLOs for different critical user journeys.. Using multiple SLOs for different critical user journeys (e.g., checkout, product search, login) allows you to tailor reliability targets to the specific performance and availability needs of each workflow. This granularity prevents a single, coarse SLO from masking issues that affect only a subset of users, enabling more precise monitoring and faster incident response.

Answer

Use a single global SLO for all customer segments.

Answer

Set the SLO to 100% to ensure maximum reliability.

Question 14

A company uses Cloud Build with a GitHub trigger to build and deploy a containerized application to GKE. They want to ensure that only code merged into the `main` branch is deployed to production, while pull requests to `main` trigger a build that runs tests but does not deploy. How should they configure the trigger?

Accepted Answer

Create two triggers: one for pull requests with a filter on `main` that runs tests only, and another for pushes to `main` that runs tests and deploys.. To deploy only on merges to `main` and run tests on pull requests, the cleanest solution is two separate triggers. One trigger handles pull request events targeting `main` and runs tests without deploying; the other handles push events to `main` and runs tests followed by deployment. This separation leverages Cloud Build's native event filtering and avoids complex conditional logic that may not be supported.

Answer

Use a single trigger with a branch filter for `main` and add a manual approval step before deployment.

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Create a single trigger with a branch filter for `main` and use a conditional build step that checks if the build was triggered by a pull request.

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Configure the trigger to fire on all branches and use a build step that checks the `$BRANCH_NAME` substitution variable to decide whether to deploy.

Question 15

Your organization is migrating a monolithic application to microservices on Cloud Run. You need to monitor the health of each microservice and aggregate logs and metrics in a central dashboard. You have set up Cloud Monitoring custom dashboards and logs-based metrics. After the initial deployment, you notice that the dashboards show data only for some services, while others appear to have no metrics. You verify that all services are running and emitting logs. What is the most likely cause?

Accepted Answer

The services are running in different GCP projects and you are viewing only one project.. Cloud Monitoring dashboards and logs-based metrics are scoped to a single GCP project. If microservices are deployed across multiple projects, metrics from services in other projects will not appear unless the dashboard is configured to aggregate data from all relevant projects. Since the question states that some services have no metrics while all are running and emitting logs, the most likely cause is that those services reside in different projects.

Answer

The services are not exporting metrics to Cloud Monitoring via the Monitoring API.

Answer

The logs-based metrics are not configured to parse logs from all services.

Answer

The Cloud Monitoring agent is not installed on the Cloud Run instances.