Professional-Cloud-Developer試験の準備方法|100%合格率のProfessional-Cloud-Developer出題内容試験|有難いGoogle Certified Professional - Cloud Developerテスト問題集

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Google Professional-Cloud-Developer Exam Syllabus Topics:
| Section | Objectives |
|---|
| Topic 1: Design scalable, available, and reliable cloud-native applications | - Design highly performant applications and APIs
- 1. Design resilient and scalable application architectures
- 2. Choose storage options for application data
- 3. Design secure applications
|
| Topic 2: Build and test applications | - Manage development environments
- 1. Build application artifacts
- 2. Implement CI/CD pipelines
- 3. Perform application testing
|
| Topic 3: Deploy applications | - Deploy applications to Google Cloud
- 1. Deploy applications to Google Kubernetes Engine
- 2. Manage application rollout strategies
- 3. Deploy applications using Cloud Run
|
| Topic 4: Integrate applications with Google Cloud services | - Integrate with data and storage services
- 1. Use messaging and event-driven services
- 2. Integrate with Google Cloud APIs
- 3. Implement authentication and authorization
|
| Topic 5: Manage application performance monitoring | - Monitor and troubleshoot applications
- 1. Optimize application performance
- 2. Configure logging and tracing
- 3. Analyze metrics and alerts
|
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Professional-Cloud-Developerテスト問題集、Professional-Cloud-Developer試験時間
Professional-Cloud-Developerトレント準備には、さまざまな資格試験の実際の質問とシミュレーションの質問が含まれています。効率的に勉強する価値があります。時間は絶え間ない発展であり、命題の専門家は命題の社会変化傾向の進行に応じて実際のProfessional-Cloud-Developer試験の質問を継続的に設定し、ホットな問題と政策変更を意識的に強調します。命題論文の方向性をよりよく把握できるようにするため、Professional-Cloud-Developerの学習問題では、最新のコンテンツに焦点を当て、Professional-Cloud-Developer試験に合格するのに役立ちます。
Google Certified Professional - Cloud Developer 認定 Professional-Cloud-Developer 試験問題 (Q120-Q125):
質問 # 120
Which service should HipLocal use for their public APIs?
- A. Cloud Armor
- B. Cloud Functions
- C. Cloud Endpoints
- D. Shielded Virtual Machines
正解:D
質問 # 121
You are deploying new workloads on a GKE Autopilot mode cluster. You need to ensure that the Pods are scheduled on nodes that use Arm architecture CPUs. The cluster currently has no Arm- based CPUs. You want to minimize cluster operations. How should you ensure that the workloads on the GKE cluster will use Arm-based CPU nodes?
- A. Apply node taints on node pools to tell GKE to only schedule your workloads on Arm-based CPU nodes.
- B. Deploy a new cluster in GKE Standard mode, and set up a node pool with Arm-based CPU nodes. Use nodeSelector in your manifest to ensure that Pods are scheduled in this node pool.
- C. Apply Pod tolerations to request GKE to avoid scheduling Pods on nodes that do not have Arm- based CPUs.
- D. Request the Scale-out compute class and the arm64 architecture in your manifest.
正解:D
解説:
Requesting the Scale-out compute class and the arm64 architecture in your manifest is the correct approach for GKE Autopilot mode, as it automatically provisions Arm-based nodes and schedules your Pods on them with minimal cluster operations.
質問 # 122
Your organization has recently begun an initiative to replatform their legacy applications onto Google Kubernetes Engine. You need to decompose a monolithic application into microservices.
Multiple instances have read and write access to a configuration file, which is stored on a shared file system. You want to minimize the effort required to manage this transition, and you want to avoid rewriting the application code. What should you do?
- A. Create a new Cloud Storage bucket, and mount it via FUSE in the container.
- B. Create a new Filestore instance, and mount the volume as an NFS PersistentVolume.
- C. Create a new ConfigMap and volumeMount to store the contents of the configuration file.
- D. Create a new persistent disk, and mount the volume as a shared PersistentVolume.
正解:B
解説:
https://kubernetes.io/docs/concepts/storage/volumes/#nfs
An nfs volume allows an existing NFS (Network File System) share to be mounted into a Pod.
Unlike emptyDir, which is erased when a Pod is removed, the contents of an nfs volume are preserved and the volume is merely unmounted. This means that an NFS volume can be pre- populated with data, and that data can be shared between pods. NFS can be mounted by multiple writers simultaneously.
質問 # 123
You work on an application that relies on Cloud Spanner as its main datastore. New application features have occasionally caused performance regressions. You want to prevent performance issues by running an automated performance test with Cloud Build for each commit made. If multiple commits are made at the same time, the tests might run concurrently. What should you do?
- A. Create a new Cloud Spanner instance for every build. Load the required data. Delete the Cloud Spanner instance after the test is run.
- B. Start the Cloud Spanner emulator locally. Load the required data. Shut down the emulator after the test is run.
- C. Create a new project with a random name for every build. Load the required data. Delete the project after the test is run.
- D. Create a project with a Cloud Spanner instance and the required data. Adjust the Cloud Build build file to automatically restore the data to its previous state after the test is run.
正解:A
解説:
Since the testing needs to accommodate scenarios where multiple commits are made simultaneously, and hence multiple tests might run concurrently, the testing environment should support isolated and independent testing instances to avoid interference among tests. Given these requirements, using the Cloud Spanner emulator would not be the best choice for this scenario. The emulator is primarily suited for local development, unit, and integration testing, and is not built for production-scale performance testing. It may not accurately replicate performance characteristics at scale or under load, which are crucial aspects in this case.
質問 # 124
Your App Engine standard configuration is as follows:
service: production
instance_class: B1
You want to limit the application to 5 instances. Which code snippet should you include in your configuration?
manual_scaling:
- A. max_instances: 5
idle_timeout: 10m - B. instances: 5
min_pending_latency: 30ms
manual_scaling: - C. max_instances: 5
idle_timeout: 10m
basic_scaling: - D. instances: 5
min_pending_latency: 30ms
basic_scaling:
正解:D
質問 # 125
......
Professional-Cloud-Developer試験の準備は精巧にまとめられており、非常に効率的です。時間と労力を節約できます。合格率とヒット率も非常に高く、数千人の受験者が当社のProfessional-Cloud-Developerガイドトレントを信頼し、試験に合格しています。候補者には非常に多くの保証を提供しており、Professional-Cloud-Developer学習教材を心配なく購入できます。そのため、当社Googleが提供するProfessional-Cloud-Developer試験トレントを十分に理解し、最初の試行でProfessional-Cloud-Developer試験に合格できることを願っています。
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