Google PCA Manage implementation of cloud architecture Practice Test 2 — 15 Questions

Question 1

A company runs a batch processing workload on Compute Engine instances in a managed instance group (MIG). The job is CPU-intensive and takes approximately 4 hours to complete. The company wants to reduce costs without sacrificing performance. Which action should they take?

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

Accepted Answer

Use preemptible VMs for the MIG and implement a checkpointing mechanism to handle interruptions.. Preemptible VMs are significantly cheaper than standard VMs but can be terminated at any time. For a batch processing workload that is CPU-intensive and runs for 4 hours, using preemptible VMs in a MIG with a checkpointing mechanism allows the job to resume from the last saved state after an interruption, thus reducing costs without sacrificing performance.

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Purchase committed use discounts for the instance type.

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Change the machine series to a smaller machine type.

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Provision additional reserved VMs to ensure capacity.

Question 2

An organization has deployed a multi-region Cloud Spanner instance for a global application. The application is experiencing high latency for read requests from a specific region. The team has verified that the application is using stale reads and the data distribution is even. What is the most likely cause of the high latency?

Accepted Answer

The Spanner instance does not have a read replica in a location close to the clients.. Cloud Spanner uses a single global configuration with regional read replicas. If the instance does not have a read replica in the region where the clients are located, read requests must traverse the network to a replica in another region, causing higher latency. Even with stale reads, the physical distance to the nearest replica directly impacts read latency.

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The number of read replicas in the region is insufficient to handle the read volume.

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The Spanner instance has too few nodes, causing contention.

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The application is using read-write transactions instead of read-only transactions.

Question 3

A company is using Cloud Load Balancing to distribute traffic to a managed instance group (MIG) of web servers. The web servers are currently running in us-central1. To improve availability, the company plans to add a second MIG in us-west1. What must be done to ensure traffic is automatically routed to the closest healthy backend?

Accepted Answer

Use a global external HTTP(S) load balancer and add both MIGs as backends.. A global external HTTP(S) load balancer can route traffic to backends in multiple regions and automatically directs requests to the closest healthy backend based on the client's geographic location and backend health. Adding both MIGs as backends to this single anycast IP ensures traffic is distributed to the nearest region without additional DNS-based routing or redirects.

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Use a Network Load Balancer in us-central1 and configure a redirect to the new MIG.

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Use an internal TCP/UDP load balancer in each region and configure DNS-based routing.

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Use an external TCP/UDP Network Load Balancer with the new MIG as an additional backend.

Question 4

A company is designing a disaster recovery plan for a critical application running on Compute Engine. The application uses a PostgreSQL database and stores files on persistent disks. The recovery time objective (RTO) is 4 hours, and the recovery point objective (RPO) is 1 hour. Which two actions should the company take?

Accepted Answer

Configure PostgreSQL replication to a standby instance in another region.. PostgreSQL replication to a standby instance in another region meets both the RPO of 1 hour (near-real-time replication keeps data loss minimal) and the RTO of 4 hours (a standby can be promoted quickly). This is a standard disaster recovery pattern for cross-region resilience, ensuring that database changes are continuously replicated with minimal lag.

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Create an instance template for the application and store it in Cloud Storage.

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Take hourly persistent disk snapshots and store them in the same region.

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Use Cloud Storage to store database backups and transfer them to a different region daily.

Question 5

Your company runs a containerized microservices application on Google Kubernetes Engine (GKE) with a regional cluster. The application consists of a frontend service, a backend API service, and a background worker service that processes messages from Cloud Pub/Sub. The worker service uses a Deployment with 3 replicas. Recently, the team noticed that the worker service is frequently failing with 'ContainerCreating' errors. The error message in the pod events is: 'Failed to pull image "gcr.io/my-project/my-worker:latest": rpc error: code = DeadlineExceeded desc = context deadline exceeded'. The image is stored in Container Registry in the same project. The cluster nodes are n1-standard-2 VMs with 10 GB of disk space. The team has confirmed that the image exists and that the nodes have internet access. What is the most likely cause of the issue?

Accepted Answer

The nodes have insufficient disk space to pull the new image, causing the pull to time out.. The error 'context deadline exceeded' when pulling an image indicates that the kubelet timed out while trying to download the container image. With only 10 GB of disk space on n1-standard-2 nodes, the node's disk may be nearly full, causing the image pull to stall or fail due to insufficient space to unpack the layers. This is the most likely cause because the image exists and internet access is confirmed, ruling out authentication or connectivity issues.

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The worker pods require node affinity to a specific node pool that is not configured.

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The nodes do not have the necessary permissions to access Container Registry.

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The cluster is a regional cluster, but the worker pods are all scheduled in the same zone, causing resource contention.

Question 6

Your organization is moving a legacy monolithic application to Google Kubernetes Engine (GKE). The application currently runs on a single virtual machine with a local MySQL database. You need to design a cloud-native architecture that improves scalability and reliability. Which two actions should you take? (Choose TWO.)

Accepted Answer

Refactor the application into microservices and deploy each as a separate deployment in GKE.. Refactoring the monolithic application into microservices and deploying each as a separate Deployment in GKE aligns with cloud-native principles, enabling independent scaling, fault isolation, and easier updates. This approach improves scalability and reliability by allowing each microservice to scale horizontally based on demand, and failures in one service do not cascade to others.

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Deploy the entire application in a single container with a large custom machine type to handle load.

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Expose the application using a simple Service of type LoadBalancer with round-robin distribution.

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Use a single Pod with multiple containers that communicate via localhost to reduce latency.

Question 7

Your company runs a critical application on Compute Engine instances in a managed instance group across three zones. The application writes logs to local disk. You are asked to improve the reliability of log retention and ensure logs are available in case of instance failure. You have already configured a health check that automatically recreates instances. However, after a recent zonal outage, logs from the affected instances were lost. You need to implement a solution that preserves logs even when instances are terminated. What should you do?

Accepted Answer

Install the Cloud Logging agent on each instance and configure it to stream application logs to Cloud Logging.. The Cloud Logging agent streams logs directly to Cloud Logging (now part of Google Cloud's operations suite), which stores logs independently of the Compute Engine instances. This ensures logs are preserved even if instances are terminated due to a zonal outage or health check recreation, as logs are sent to a centralized, durable logging service rather than being stored on local disk.

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Increase the size of the local SSD to accommodate more logs and set a longer retention period.

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Configure each instance to write logs to a persistent disk that is retained after instance deletion.

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Mount a Cloud Storage bucket using gcsfuse on each instance and write logs directly to the bucket.

Question 8

A company is deploying a microservices application on Google Kubernetes Engine (GKE). They want to ensure that the cluster can automatically scale based on custom metrics, such as the number of pending requests per pod. Which two steps should they take? (Choose TWO)

Accepted Answer

Deploy the Metrics Server in the cluster to expose custom metrics via the Custom Metrics API.. The Metrics Server is required to expose custom metrics via the Custom Metrics API in GKE. Without it, the HorizontalPodAutoscaler (HPA) cannot retrieve the custom metrics needed for scaling decisions. Option B is correct because the application must expose custom metrics (e.g., pending requests) through an endpoint, and the HPA must be configured to reference that custom metric name to trigger scaling based on that specific value.

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Enable the Cloud Monitoring API and create a custom dashboard to track pending requests.

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Configure a HorizontalPodAutoscaler (HPA) with the target average CPU utilization set to 80%.

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Enable GKE Autopilot mode to automatically manage scaling based on custom metrics.

Question 9

A developer runs the command above and sees the output. The cluster has one node pool with 3 nodes, each of type e2-standard-4 (4 vCPU, 16 GB RAM). The application requires at least 2 GB of memory per pod and the cluster has 10 pods that need to be scheduled. The developer also notices that the node pool autoscaling is enabled with a minimum of 1 and maximum of 5 nodes. However, the cluster is unable to schedule all pods. What is the most likely cause?

Accepted Answer

The pod resource requests exceed the allocatable resources on the existing nodes after accounting for system reservations.. The cluster has 3 e2-standard-4 nodes, each with 4 vCPU and 16 GB RAM. After accounting for system reservations (e.g., kubelet, OS, daemonsets), the allocatable memory per node is typically around 13-14 GB. With 10 pods each requesting 2 GB, the total memory request is 20 GB, but the total allocatable memory across 3 nodes is only about 39-42 GB. However, the autoscaler can only scale up to 5 nodes, and even then, the total allocatable memory would be around 65-70 GB, which is sufficient. The most likely cause is that the pod resource requests exceed the allocatable resources on the existing nodes, preventing scheduling, and the autoscaler may not have triggered yet or is constrained by other factors like CPU or node limits.

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The cluster is running an older version of Kubernetes that does not support node auto-scaling.

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The node pool autoscaler is not properly configured to scale up based on pod resource requests.

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The node auto-repair feature is disabled, causing a node to be unhealthy.

Question 10

A large e-commerce company runs a multi-tier application on Google Cloud. The frontend is served by a global HTTP Load Balancer with a backend service pointing to a managed instance group (MIG) of nginx web servers. The application tier consists of a regional internal TCP/UDP load balancer distributing traffic to a MIG of Java application servers. The database tier uses Cloud SQL for PostgreSQL in a failover replica configuration. The architecture is deployed in the us-central1 region across three zones. Recently, the operations team noticed intermittent 502 Bad Gateway errors from the frontend load balancer during peak traffic hours. The errors last for a few minutes and then recover. The team suspects the application tier is overwhelmed. They need to implement a solution that can handle traffic spikes without manual intervention. Which course of action should they take?

Accepted Answer

Configure HTTP health checks on the regional internal load balancer and set the autoscaler to use the 'HTTP load balancing utilization' metric for the application tier MIG.. The intermittent 502 errors during peak traffic indicate that the application tier MIG is being overwhelmed. By configuring HTTP health checks on the regional internal load balancer and setting the autoscaler to use the 'HTTP load balancing utilization' metric, the autoscaler can scale the application tier MIG based on the actual load distribution from the internal load balancer, ensuring it handles traffic spikes without manual intervention. This directly addresses the root cause—insufficient application instances—by enabling dynamic scaling based on real-time utilization.

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Increase the maximum number of instances in the application tier MIG from 10 to 20.

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Enable Cloud Armor on the frontend load balancer with a rate-limiting rule to block excessive traffic.

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Enable Cloud CDN on the frontend load balancer to cache static assets and reduce load on the application tier.

Question 11

Drag and drop the steps to deploy a containerized application to Google Kubernetes Engine (GKE) using a Deployment into the correct order.

Accepted Answer

Build the container image, then push the image to a registry, then create a Deployment manifest, then apply the Deployment to the cluster, then create a Service to expose the application.. The image must be in a registry before the Deployment can reference it. The Service provides external access.

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Build the container image, then push the image to a registry, then create a Service, then create a Deployment manifest, then apply the Deployment.

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Build the container image, then create a Deployment manifest, then push the image to a registry, then apply the Deployment, then create a Service.

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Create a Deployment manifest, then apply the Deployment, then build the container image, then push the image to a registry, then create a Service.

Question 12

Match each IAM role type to its description.

Accepted Answer

Basic roles: Broad roles that apply to all resources in a project (Owner, Editor, Viewer). In GCP, IAM roles are categorized into basic (broad), predefined (service-specific), and custom (user-defined). Common confusions arise between predefined and custom roles.

Answer

Basic roles: Roles curated by Google Cloud that provide fine-grained access for specific services

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Predefined roles: Roles that you create to tailor permissions to your organization's specific needs

Question 13

A company is migrating a monolithic application to Google Cloud. They want to minimize changes to the application code while taking advantage of Cloud Run for serverless containers. Which approach should they take?

Accepted Answer

Use Cloud Run by packaging the existing application as a container and listening on a web server.. Cloud Run can run any containerized application that listens on HTTP requests on port 8080. By packaging the existing monolithic application as a container and adding a lightweight web server (e.g., Express, Flask, or Nginx), the company can deploy it to Cloud Run with minimal code changes, leveraging serverless scaling and pay-per-use pricing without refactoring into microservices.

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Deploy the application to App Engine standard environment with automatic scaling.

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Lift and shift the application to Compute Engine instances behind a load balancer.

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Refactor the application into microservices and deploy each as a separate Cloud Run service.

Question 14

An organization has a multi-regional deployment of a stateful application on GKE using regional persistent disks. They need to implement disaster recovery with an RPO of less than 1 hour and RTO of 30 minutes. What is the most cost-effective approach?

Accepted Answer

Use regional persistent disks with asynchronous replication to a secondary region and deploy GKE clusters in both regions with a load balancer directing traffic.. Regional persistent disks with asynchronous replication provide built-in, managed replication to a secondary region, meeting the RPO of less than 1 hour and RTO of 30 minutes without additional infrastructure costs. By deploying GKE clusters in both regions and using a load balancer, traffic can be redirected to the secondary cluster within the RTO, making this the most cost-effective approach as it avoids third-party tools or complex manual processes.

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Use zonal persistent disks and take snapshots every 45 minutes, then restore in secondary region.

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Use a third-party replication tool to asynchronously replicate data to the secondary region.

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Use Cloud Storage FUSE to write state to a multi-regional bucket and read from secondary cluster.

Question 15

A DevOps team is building a CI/CD pipeline for a microservices application deployed on Google Kubernetes Engine. They want to ensure that each microservice can be deployed independently without affecting other services. Which strategy should they use?

Accepted Answer

Implement canary deployments with a service mesh such as Istio and use separate Cloud Build triggers per microservice.. It combines canary deployments with a service mesh (Istio) to gradually shift traffic to a new version of a single microservice, ensuring independent deployment without impacting other services. Separate Cloud Build triggers per microservice allow each service to be built and deployed independently, aligning with the microservices architecture's requirement for decoupled release cycles.

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Use blue/green deployments with a global load balancer to switch traffic.

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Use Cloud Deploy with rollout strategies and keep all microservices in the same GKE namespace.

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Create a single monolithic pipeline that deploys all microservices simultaneously.