Google PCA Ensure solution and operations reliability Practice Test 8 — 15 Questions

Question 1

A company uses Cloud Storage for backup data. They want to protect against accidental deletion. Which option is best?

Accepted Answer

Enable object versioning.. Object versioning in Cloud Storage preserves every version of an object, including overwrites and deletions. When versioning is enabled, a delete operation creates a delete marker instead of permanently removing the object, allowing easy recovery. This directly protects against accidental deletion by retaining all previous object versions.

Answer

Use a lifecycle policy.

Answer

Set a retention policy.

Answer

Use object holds.

Question 2

A company runs a stateful application on GKE using StatefulSets. Which THREE practices improve reliability?

Accepted Answer

Use headless services.. A headless service (clusterIP: None) allows direct pod-to-pod communication without load balancing, which is essential for stateful applications like databases that require stable network identities. Each pod in a StatefulSet gets a unique DNS name (e.g., pod-0.service.namespace.svc.cluster.local), enabling reliable discovery and ordering for replication, leader election, and failover. This ensures that clients always reach the correct pod instance, improving overall reliability.

Answer

Use horizontal autoscaling based on disk usage.

Answer

Use persistent volumes with reclaim policy Delete.

Question 3

A company runs a critical application on Compute Engine instances in a managed instance group (MIG) with autoscaling. Users report intermittent 503 errors during traffic spikes. Which action should the company take to improve reliability?

Accepted Answer

Configure a health check with a sufficient initial delay (grace period) in the MIG. Intermittent 503 errors during traffic spikes often indicate that new VM instances are being started but are not yet ready to serve traffic, causing the load balancer to forward requests to them prematurely. Configuring a health check with a sufficient initial delay (grace period) in the MIG ensures that newly created instances are given time to fully initialize and pass health checks before they receive traffic, preventing 503 errors. This directly addresses the root cause by allowing the application to become healthy before being added to the load balancer's backend.

Answer

Change the load balancer from regional to global

Answer

Increase the autoscaling cool-down period from 60s to 120s

Answer

Increase the maximum number of instances in the MIG

Question 4

A company runs a microservices-based application on Google Kubernetes Engine (GKE) with a Regional cluster. They want to improve reliability by implementing best practices for pod scheduling and resilience. Which TWO actions should they take? (Choose two.)

Accepted Answer

Define a PodDisruptionBudget for each deployment to limit the number of concurrent disruptions. A PodDisruptionBudget (PDB) limits the number of Pods of a replicated application that can be down simultaneously from voluntary disruptions, such as node maintenance or cluster upgrades. This ensures that a minimum number of replicas remain available, improving application reliability during planned events.

Answer

Set terminationGracePeriodSeconds to 0 for faster pod termination during scale-down

Answer

Enable cluster autoscaler to automatically add nodes when pods are pending

Answer

Set resource requests equal to limits to ensure guaranteed QoS class

Question 5

A company runs a stateful workload on Compute Engine with regional persistent disks (PD). They need to implement a disaster recovery (DR) plan with a Recovery Point Objective (RPO) of less than 1 hour and Recovery Time Objective (RTO) of less than 4 hours. Which THREE steps should they include in their DR plan? (Choose three.)

Accepted Answer

Take snapshots of the persistent disk every 30 minutes and copy them to a Cloud Storage bucket in another region. Taking snapshots every 30 minutes meets the RPO of less than 1 hour. By copying these snapshots to a Cloud Storage bucket in another region, you ensure data is available in a DR region for recovery, which is essential for cross-region disaster recovery.

Answer

Create a snapshot schedule for the persistent disk every 4 hours

Answer

Use regional persistent disks to automatically replicate data to a second zone

Question 6

You are the lead cloud architect for a startup that runs a web application on Google Kubernetes Engine (GKE) with a standard (zonal) cluster. The application is deployed with 3 replicas of a stateless frontend service. During a recent incident, a zone outage caused all GKE nodes to become unavailable, leading to application downtime of 45 minutes. You need to redesign the cluster to tolerate a single zone failure with no more than 5 minutes of downtime. Your budget allows for at most a 20% increase in compute costs. Which approach should you take?

Accepted Answer

Migrate the cluster to a regional GKE cluster with nodes in 3 zones and distribute replicas across zones. D is correct because a regional GKE cluster distributes nodes across three zones, ensuring that if one zone fails, the remaining two zones continue serving traffic. By spreading the 3 replicas across zones (e.g., one per zone), the application tolerates a single zone outage with near-zero downtime, and the 20% cost increase covers the additional node pool overhead without exceeding the budget.

Answer

Increase the number of replicas from 3 to 9 and keep the zonal cluster

Answer

Change the frontend deployment to use regional persistent disks

Answer

Deploy second GKE cluster in another region and use global load balancer for failover

Question 7

Your company runs a customer-facing API on Cloud Run with a concurrency setting of 80. The API calls a backend Cloud Function that performs a heavy computation (2–5 seconds). During peak hours, the API experiences increased latency and some requests time out after 60 seconds. Monitoring shows that the Cloud Run max instances is set to 100, and the Cloud Function max instances is set to 10. The timeout for Cloud Run is set to 300 seconds. The Cloud Function's timeout is set to 540 seconds. You need to reduce end-to-end latency and prevent timeouts while minimizing cost. Which action is most effective?

Accepted Answer

Increase Cloud Function max instances from 10 to 100. The bottleneck is the Cloud Function's low max instances (10), causing queuing. Increasing Cloud Function max instances allows more concurrent requests to be processed, reducing latency and timeouts. Option A is wrong because concurrency on Cloud Run is separate from backend; reducing concurrency would require more Cloud Run containers and increase cost. Option B is wrong because increasing Cloud Run max instances alone doesn't help if Cloud Function capacity is the limit. Option D is wrong because increasing Cloud Run timeout doesn't reduce latency; it just keeps the connection alive longer.

Answer

Increase Cloud Run max instances from 100 to 500

Answer

Increase Cloud Run request timeout from 300 to 600 seconds

Answer

Reduce Cloud Run concurrency from 80 to 10

Question 8

Your company runs a data pipeline on Google Cloud using Cloud Dataflow for streaming processing from Pub/Sub to BigQuery. The pipeline writes to a BigQuery table partitioned by day. The data is used for real-time dashboards. Recently, a spike in traffic caused the Dataflow pipeline to fall behind, and the dashboard displayed stale data. You need to design the pipeline to handle traffic spikes without data loss or long delays. The pipeline must be cost-efficient and use defaults where possible. Which solution should you implement?

Accepted Answer

Enable autoscaling in the Dataflow pipeline and use Streaming Engine to handle larger throughput. Enabling autoscaling in Dataflow allows the pipeline to dynamically adjust the number of workers based on the processing backlog, while Streaming Engine offloads the shuffle and state storage to Google-managed resources, reducing the impact of traffic spikes. This combination ensures the pipeline can scale up quickly to handle increased throughput without data loss or long delays, and it remains cost-efficient by scaling down when demand decreases.

Answer

Modify the pipeline to use a batch (non-streaming) approach, writing hourly batches from Pub/Sub to BigQuery

Answer

Create a Cloud Scheduler job that increases the number of Dataflow workers every 5 minutes based on Pub/Sub subscription backlog

Answer

Change the Dataflow worker machine type from n1-standard-4 to n1-highmem-8

Question 9

A financial services company is migrating a monolithic Java application to Google Kubernetes Engine (GKE) for improved scalability and reliability. The application serves real-time trading data and has strict latency requirements. Post-migration, the team observes frequent pod restarts due to OutOfMemory (OOM) errors, increased latency during peak trading hours, and occasional database connection timeouts. The current setup uses a single GKE cluster with a node pool of n1-standard-4 machines, a stateless application deployed as a Deployment with resource requests and limits set to 512 Mi memory and 1 CPU. The database is a Cloud SQL PostgreSQL instance with 2 vCPUs and 7.5 GB memory, and applications connect using a hardcoded connection string. The team wants to ensure reliable operation under load and during node maintenance events. Which course of action best addresses the reliability issues?

Accepted Answer

Adjust resource requests to 1 Gi memory and 2 CPU, set limits to 2 Gi and 4 CPU, create an HPA based on a custom metric (e.g., requests per second), enable cluster autoscaler, implement Cloud SQL connection pooling via Cloud SQL Auth Proxy with a max connection pool size, and configure PDB with maxUnavailable 1.. Best addresses all reliability issues. Adjusting resource requests to 1 Gi memory and 2 CPU ensures proper scheduling, while limits of 2 Gi and 4 CPU prevent OOM errors. The HPA based on custom metrics (e.g., requests per second) scales pods proactively during peak trading hours. Cluster autoscaler handles node capacity, and Cloud SQL connection pooling via Cloud SQL Auth Proxy with a max pool size prevents database connection timeouts. Finally, a PDB with maxUnavailable 1 ensures availability during node maintenance. Option B misses resource tuning, autoscaling, and connection pooling. Option C unnecessarily moves the database to GKE, increasing complexity and losing managed DB benefits. Option D lacks custom metric HPA, cluster autoscaler, and PDB, leaving gaps in scalability and maintenance handling.

Answer

Enable GKE node auto-upgrade, configure Pod Disruption Budgets (PDB) with minAvailable 1, and set readiness probes to check application health.

Answer

Migrate the database to a StatefulSet in GKE with persistent volumes, increase node count to 10, and enable cluster autoscaler.

Answer

Increase memory limits to 2 Gi and CPU to 2, add Horizontal Pod Autoscaler (HPA) based on CPU utilization, and implement connection pooling using Cloud SQL Auth Proxy.

Question 10

Refer to the exhibit. An application running on a GCE instance (ID: 1234567890) is unable to connect to a database at 10.0.0.1:5432. The logs show repeated 'Connection refused' errors. What is the most likely cause?

Accepted Answer

The database service is not running or is not listening on port 5432.. The 'Connection refused' error indicates that the TCP handshake was rejected by the target host, which typically means the database service is not actively listening on port 5432. This is distinct from a firewall block, which would result in a timeout or 'no route to host' error. Since the error is immediate and specific to port 5432, the most likely cause is that the PostgreSQL or other database service is not running or is bound to a different interface/port.

Answer

The firewall rule allowing traffic on port 5432 is missing or misconfigured.

Answer

The instance is using an outdated SSL certificate.

Answer

The VPC network has no route to the database subnet.

Question 11

Refer to the exhibit. A Cloud Deploy pipeline has a release with two targets: staging and prod. The staging rollout succeeded, but the prod rollout failed with 'MANIFEST_INVALID'. What is the most likely cause of the failure?

Accepted Answer

The manifest for prod contains a syntax error or references a resource that does not exist in the prod cluster.. The 'MANIFEST_INVALID' error in Cloud Deploy indicates that the Kubernetes manifest provided for the prod target is syntactically incorrect or references a resource (e.g., a ConfigMap, Secret, or custom resource definition) that does not exist in the prod cluster. This is a validation failure that occurs before any deployment attempt, so it is not related to runtime issues like permissions or quotas.

Answer

The prod target's applyManifest has a higher replica count than staging, which violates a cluster quota.

Answer

The prod cluster does not have the necessary permissions to pull the container image.

Answer

The release was not approved for the prod target.

Question 12

A retail company uses a Cloud SQL for MySQL instance with a single zone. The database is critical for order processing, and the company wants to minimize downtime if the zone hosting the instance fails. They also want to ensure that the application can continue to write data during a zonal failure without manual intervention. What should they do?

Accepted Answer

Enable high availability by configuring the instance as regional.. Configuring Cloud SQL as regional provides a synchronous standby in another zone and automatic failover. This ensures that writes can continue with minimal downtime during a zonal failure, satisfying the business requirement. Other options either require manual intervention, result in data loss, or do not provide automatic failover.

Answer

Migrate the database to a Compute Engine instance with a regional persistent disk.

Answer

Create a read replica in another zone and promote it if the primary fails.

Answer

Take regular automated backups and restore to a new instance in another zone.

Question 13

You are deploying a new version of a microservice to Google Kubernetes Engine (GKE). The service must remain available during the rollout, and you need to minimize the risk of exposing bugs to all users at once. You want to gradually shift traffic to the new version while monitoring key metrics. Which strategy should you use?

Accepted Answer

Implement a canary deployment using Istio or Anthos Service Mesh to route a small percentage of traffic to the new version.. A canary deployment using a service mesh allows you to route a small portion of traffic to the new version, monitor its behavior, and then gradually increase traffic or roll back if problems occur. This minimizes risk and maintains availability. Other strategies either switch all traffic at once or lack the granular control needed for gradual, metric-based rollouts.

Answer

Perform a rolling update by changing the Deployment's pod template.

Answer

Use a blue/green deployment by creating a new Deployment and switching the Service selector.

Answer

Create a new GKE cluster and deploy the new version there, then update DNS to point to the new cluster.

Question 14

A retail company runs a public-facing API on Cloud Run in the europe-west1 region. During a marketing campaign, traffic tripled within minutes. The service remained available, but some requests returned HTTP 503 errors. The team wants to reduce the chance of 503 errors during future traffic spikes while keeping the deployment simple. What should they do?

Accepted Answer

Set the Cloud Run service's maximum number of instances to a high value and configure a minimum number of instances greater than zero.. The 503 errors occurred because the service hit its configured maximum instance count during the spike. Raising the maximum instance limit lets Cloud Run create enough instances to absorb the burst, and setting a minimum instance count above zero keeps warm instances available so the initial surge does not fail while new instances start. Resource limits, CDN caching, and multi-region deployment do not directly remove the instance ceiling that caused the rejections.

Answer

Increase the container's allocated memory and CPU limits in the Cloud Run revision settings.

Answer

Enable Cloud CDN on the Cloud Run service and set a long cache TTL for all responses.

Answer

Deploy the service to multiple Cloud Run regions and use a global external Application Load Balancer with a serverless network endpoint group.

Question 15

A retail company runs a customer-facing API on a managed instance group (MIG) of Compute Engine VMs behind an external Application Load Balancer. The SRE team wants the load balancer to stop sending traffic to a VM as soon as the local application health endpoint starts returning HTTP 500, even before the VM is fully unresponsive. Which load balancer component must be configured to achieve this?

Accepted Answer

Configure an HTTP health check with a check interval and unhealthy threshold on the backend service, and attach it to the MIG.. For an external Application Load Balancer, the backend service's health check is the mechanism that determines which instances receive traffic. By pointing an HTTP health check at the application's health endpoint, a VM that begins returning HTTP 500 is marked unhealthy once the unhealthy threshold is met and is removed from rotation, so users are not routed to a failing instance while it is still alive.

Answer

Set the MIG's autoscaling policy to scale in when CPU utilization drops below a target threshold.

Answer

Enable Cloud CDN on the backend service so that failing responses are cached and served from edge locations.

Answer

Create an uptime check in Cloud Monitoring against the API endpoint and rely on its alerting policy to failover.