Professional-Cloud-Developerシュミレーション問題集、Professional-Cloud-Developer参考書

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Google Professional-Cloud-Developer Exam Syllabus Topics:
| Section | Objectives |
|---|
| Topic 1: Build and test applications | - Manage development environments
- 1. Implement CI/CD pipelines
- 2. Build application artifacts
- 3. Perform application testing
|
| Topic 2: Integrate applications with Google Cloud services | - Integrate with data and storage services
- 1. Implement authentication and authorization
- 2. Integrate with Google Cloud APIs
- 3. Use messaging and event-driven services
|
| Topic 3: Deploy applications | - Deploy applications to Google Cloud
- 1. Manage application rollout strategies
- 2. Deploy applications to Google Kubernetes Engine
- 3. Deploy applications using Cloud Run
|
| Topic 4: Design scalable, available, and reliable cloud-native applications | - Design highly performant applications and APIs
- 1. Design secure applications
- 2. Design resilient and scalable application architectures
- 3. Choose storage options for application data
|
| Topic 5: Manage application performance monitoring | - Monitor and troubleshoot applications
- 1. Optimize application performance
- 2. Configure logging and tracing
- 3. Analyze metrics and alerts
|
>> Professional-Cloud-Developerシュミレーション問題集 <<
Professional-Cloud-Developer参考書、Professional-Cloud-Developer日本語版トレーリング
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Google Certified Professional - Cloud Developer 認定 Professional-Cloud-Developer 試験問題 (Q353-Q358):
質問 # 353
Your application is controlled by a managed instance group. You want to share a large read-only data set between all the instances in the managed instance group. You want to ensure that each instance can start quickly and can access the data set via its filesystem with very low latency.
You also want to minimize the total cost of the solution.
What should you do?
- A. Move the data to a Compute Engine persistent disk, and attach the disk in read-only mode to multiple Compute Engine virtual machine instances.
- B. Move the data to a Cloud Storage bucket, and mount the bucket on the filesystem using Cloud Storage FUSE.
- C. Move the data to a Cloud Storage bucket, and copy the data to the boot disk of the instance via a startup script.
- D. Move the data to a Compute Engine persistent disk, take a snapshot, create multiple disks from the snapshot, and attach each disk to its own instance.
正解:A
解説:
https://cloud.google.com/compute/docs/disks/sharing-disks-between-vms#use-multi-instances Share a disk in read-only mode between multiple VMs Sharing static data between multiple VMs from one persistent disk is "less expensive" than replicating your data to unique disks for individual instances.
https://cloud.google.com/compute/docs/disks/gcs-buckets#mount_bucket
Mounting a bucket as a file system
You can use the Cloud Storage FUSE tool to mount a Cloud Storage bucket to your Compute Engine instance. The mounted bucket behaves similarly to a persistent disk even though Cloud Storage buckets are object storage.
質問 # 354
Your team has created an application that is hosted on a Google Kubemetes Engine (GKE) cluster. You need to connect the application to a legacy REST service that is deployed in two GKE clusters in two different regions. You want to connect your application to the legacy service in a way that is resilient and requires the fewest number of steps. You also want to be able to run probe-based health checks on the legacy service on a separate port. How should you set up the connection? (Choose two.)
- A. Configure the legacy service's firewall to allow health checks originating from the Traffic Director control plane.
- B. Set up a proxyless Traffic Director configuration for the application.
- C. Configure the legacy service's firewall to allow health checks originating from the application.
- D. Configure the legacy service's firewall to allow health checks originating from the sidecar proxy.
- E. Use Traffic Director with a sidecar proxy to connect the application to the service.
正解:D、E
解説:
For connecting a GKE-hosted application to a legacy REST service across two regions, using Traffic Director with a sidecar proxy and configuring the legacy service's firewall for health checks offers a resilient and efficient solution.
https://thenewstack.io/google-traffic-director-and-the-l7-internal-load-balancer-intermingles-cloud- native-and-legacy-workloads/
質問 # 355
Your application takes an input from a user and publishes it to the user's contacts. This input is stored in a table in Cloud Spanner. Your application is more sensitive to latency and less sensitive to consistency.
How should you perform reads from Cloud Spanner for this application?
- A. Perform Read-Only transactions.
- B. Perform stale reads using single-read methods.
- C. Perform stale reads using read-write transactions.
- D. Perform strong reads using single-read methods.
正解:C
質問 # 356
One of your deployed applications in Google Kubernetes Engine (GKE) is having intermittent performance issues. Your team uses a third-party logging solution. You want to install this solution on each node in your GKE cluster so you can view the logs. What should you do?
- A. Use SSH to connect to the GKE node, and install the software manually
- B. Modify your container image to include the monitoring software
- C. Deploy the third-party solution as a DaemonSet
- D. Deploy the third-party solution using Terraform and deploy the logging Pod as a Kubernetes Deployment
正解:C
解説:
https://cloud.google.com/kubernetes-engine/docs/concepts/daemonset#usage_patterns DaemonSets are useful for deploying ongoing background tasks that you need to run on all or certain nodes, and which do not require user intervention. Examples of such tasks include storage daemons like ceph, log collection daemons like fluent-bit, and node monitoring daemons like collectd.
質問 # 357
You are developing a dashboard that aggregates temperature readings from thousands of IoT devices monitoring a city's ambient temperature. You expect a large amount of viewing traffic resulting in a large amount of data egress once the dashboard is live. The dashboard temperature display data doesn't need to be real-time and can tolerate a few seconds of lag. You decide to deploy Memorystore for Redis as the storage backend. You want to ensure that the dashboard will be highly available. How should you configure the service in Memorystore for Redis?
- A. Configure Memorystore to use read replicas.
- B. Set up Serverless VPC Access to avoid receiving traffic over the internet.
- C. Update Memorystore for Redis to the latest version.
- D. Use Private Service Access to enable low-latency network throughput.
正解:A
解説:
Using read replicas in Memorystore for Redis enhances availability by providing redundancy. If the primary instance experiences downtime, read replicas can continue serving requests, ensuring that the dashboard remains accessible. Read replicas also help distribute read traffic, which can be beneficial for high-demand applications, even though Redis itself is an in-memory store with fast access.
- Updating to the latest version may improve performance or security but does not directly improve availability.
- Private Service Access and Serverless VPC Access are network configurations that reduce latency and secure connections but do not inherently improve availability.
質問 # 358
......
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