Google Associate-Cloud-Engineer参考資料、Associate-Cloud-Engineer試験参考書

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Google Associate-Cloud-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Planning and Configuring a Cloud Solution20-25%- Planning and configuring data storage options
  • 1. Selecting appropriate storage classes
  • 2. Choosing between Cloud Storage, Filestore, Persistent Disk
- Planning and estimating GCP product usage
  • 1. Using GCP Pricing Calculator
  • 2. Reading documentation for compute, storage, and network resources
- Planning and configuring compute resources
  • 1. Configuring persistent disks and local SSDs
  • 2. Selecting appropriate machine types and zones
- Planning and configuring network resources
  • 1. Designing VPC networks
  • 2. Configuring load balancing and firewall rules
Configuring Access and Security15-20%- Viewing audit logs
  • 1. Configuring audit log policies
  • 2. Accessing and analyzing Cloud Audit Logs
- Managing service accounts
  • 1. Using service account keys
  • 2. Assigning service accounts to resources
  • 3. Creating and managing service accounts
- Managing IAM resources
  • 1. Creating and managing IAM roles
  • 2. Defining custom IAM roles
  • 3. Applying IAM policies to resources
Setting Up a Cloud Solution Environment20-25%- Managing billing configuration
  • 1. Linking billing to projects
  • 2. Setting up budget alerts and notifications
  • 3. Creating billing accounts
- Setting up cloud projects and accounts
  • 1. Creating and managing projects
  • 2. Assigning IAM roles to users and groups
  • 3. Managing billing budgets and alerts
Deploying and Implementing a Cloud Solution20-25%- Deploying and managing Compute Engine resources
  • 1. Creating and managing instances with startup scripts
  • 2. Creating and managing managed instance groups
  • 3. Creating and managing custom machine types
- Deploying applications with Cloud Deployment Manager
  • 1. Creating deployment configurations
  • 2. Using templates for resource provisioning
- Deploying and managing Kubernetes Engine resources
  • 1. Deploying workloads with Deployments and Services
  • 2. Configuring kubectl for cluster access
  • 3. Creating and managing GKE clusters
- Deploying and managing Cloud Run resources
  • 1. Deploying containerized applications to Cloud Run
  • 2. Configuring memory limits and scaling settings
- Deploying and managing Cloud Functions
  • 1. Creating and triggering Cloud Functions
  • 2. Configuring triggers and environment variables
Ensuring Successful Operation15-20%- Managing Kubernetes Engine resources
  • 1. Scaling clusters and node pools
  • 2. Managing workloads (Deployments, Services, HPA)
  • 3. Upgrading clusters
- Managing Cloud Run resources
  • 1. Configuring resource limits
  • 2. Managing revisions and traffic splitting
- Managing logging and monitoring
  • 1. Using Cloud Monitoring and Logging
  • 2. Viewing and analyzing logs
  • 3. Creating metrics and alerts
- Managing storage and data solutions
  • 1. Configuring CORS and encryption
  • 2. Setting lifecycle policies
  • 3. Managing Cloud Storage buckets and objects
- Managing Compute Engine resources
  • 1. Attaching and detaching disks
  • 2. Managing instances, disks, and snapshots
  • 3. Resizing instances and persistent disks

>> Google Associate-Cloud-Engineer参考資料 <<

Associate-Cloud-Engineer試験参考書 & Associate-Cloud-Engineer問題と解答

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Google Associate Cloud Engineer Exam 認定 Associate-Cloud-Engineer 試験問題 (Q401-Q406):

質問 # 401
Your company has a Google Cloud Platform project that uses BigQuery for data warehousing. Your data science team changes frequently and has few members. You need to allow members of this team to perform queries. You want to follow Google-recommended practices. What should you do?

正解：D

解説：
Read the dataset's metadata and to list tables in the dataset. Read data and metadata from the dataset's tables. When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.
BigQuery Data Viewer
(roles/bigquery.dataViewer)
When applied to a table or view, this role provides permissions to:
Read data and metadata from the table or view.
This role cannot be applied to individual models or routines.
When applied to a dataset, this role provides permissions to:
Read the dataset's metadata and list tables in the dataset.
Read data and metadata from the dataset's tables.
When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.
Lowest-level resources where you can grant this role:
Table
View
BigQuery Job User
(roles/bigquery.jobUser)
Provides permissions to run jobs, including queries, within the project.
Lowest-level resources where you can grant this role:
Project
to run jobs https://cloud.google.com/bigquery/docs/access-control#bigquery.jobUser databaseUser needs additional role permission to run jobs https://cloud.google.com/spanner/docs/iam#spanner.databaseUser


質問 # 402
Your company completed the acquisition of a startup and is now merging the IT systems of both companies.
The startup had a production Google Cloud project in their organization. You need to move this project into your organization and ensure that the project is billed lo your organization. You want to accomplish this task with minimal effort. What should you do?

正解：C


質問 # 403
You have designed a solution on Google Cloud Platform (GCP) that uses multiple GCP products. Your company has asked you to estimate the costs of the solution. You need to provide estimates for the monthly total cost. What should you do?

正解：A


質問 # 404
Your development team needs a new Jenkins server for their project. You need to deploy the server using the fewest steps possible. What should you do?

正解：A

解説：
Reference:
https://cloud.google.com/solutions/using-jenkins-for-distributed-builds-on-compute-engine


質問 # 405
Your company has a large quantity of unstructured data in different file formats. You want to perform ETL transformations on the data. You need to make the data accessible on Google Cloud so it can be processed by a Dataflow job. What should you do?

正解：B


質問 # 406
......

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