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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Configuring access and security	17.5%	- Managing data security 1. Enable encryption 2. Manage access to data - Configuring network security 1. Configure private access 2. Secure VPC networks 3. Manage security controls - Managing IAM 1. Implement least privilege access 2. Define roles and permissions 3. Manage service accounts
Topic 2: Planning and configuring a cloud solution	17.5%	- Planning and configuring network resources 1. Design VPC networks 2. Plan hybrid connectivity 3. Choose load balancing options 4. Plan firewall rules - Planning and configuring storage resources 1. Select storage classes 2. Plan data durability and availability - Planning and configuring compute resources 1. Plan instance configuration 2. Plan data storage solutions 3. Select compute options 4. Choose storage options
Topic 3: Ensuring successful operation of a cloud solution	20%	- Monitoring and logging 1. Set up alerts 2. Use Cloud Monitoring 3. Use Cloud Logging - Managing resources 1. Troubleshoot common issues 2. Scale resources 3. Update configurations - Managing cost and performance 1. Analyze cost reports 2. Optimize resource usage
Topic 4: Setting up a cloud solution environment	20%	- Setting up cloud projects and accounts 1. Apply organizational policies 2. Manage IAM roles and permissions 3. Enable APIs 4. Manage quotas and limits 5. Manage users and groups 6. Create resource hierarchy - Managing billing configuration 1. Create and manage billing accounts 2. Set budgets and alerts 3. Configure billing exports 4. Link projects to billing accounts
Topic 5: Deploying and implementing a cloud solution	25%	- Deploying and implementing serverless resources 1. Create Cloud Functions 2. Deploy Cloud Run services - Deploying and implementing Google Kubernetes Engine resources 1. Configure networking for GKE 2. Deploy workloads 3. Create and manage clusters - Deploying and implementing data solutions 1. Provision databases 2. Set up data processing pipelines - Deploying and implementing Compute Engine resources 1. Manage images and snapshots 2. Configure access and security 3. Launch virtual machines 4. Create managed instance groups

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Google Associate Cloud Engineer Exam 認定 Associate-Cloud-Engineer 試験問題 (Q158-Q163):

質問 # 158
You need to set up permissions for a set of Compute Engine instances to enable them to write data into a particular Cloud Storage bucket. You want to follow Google-recommended practices.
What should you do?

A. Create a service account with an access scope.
Use the access scope `https://www.googleapis.com/ auth/devstorage.write_only'.
B. Create a service account with an access scope.
Use the access scope `https://www.googleapis.com/ auth/cloud-platform'.
C. Create a service account and add it to the IAM role `storage.objectAdmin' for that bucket.
D. Create a service account and add it to the IAM role `storage.objectCreator' for that bucket.

正解：B

解説：
There are many access scopes that you can choose from but you can also just set the cloud- platform access scope, which is an OAuth scope for all Google Cloud services, and then securely limit the service account's access by granting it IAM roles.
https://cloud.google.com/compute/docs/access/service-accounts

質問 # 159
You are about to deploy a new Enterprise Resource Planning (ERP) system on Google Cloud. The application holds the full database in-memory for fast data access, and you need to configure the most appropriate resources on Google Cloud for this application. What should you do?

A. Provision Compute Engine instances with local SSDs attached.
B. Provision preemptible Compute Engine instances.
C. Provision Compute Engine instances with M1 machine type.
D. Provision Compute Engine instances with GPUs attached.

正解：C

解説：
M1 machine series Medium in-memory databases such as SAP HANA Tasks that require intensive use of memory with higher memory-to-vCPU ratios than the general-purpose high-memory machine types.
In-memory databases and in-memory analytics, business warehousing (BW) workloads, genomics analysis, SQL analysis services. Microsoft SQL Server and similar databases.
https://cloud.google.com/compute/docs/machine-types
https://cloud.google.com/compute/docs/machine-types#:~:text=databases%20such%20as-,SAP%20HANA,-In%
https://www.sap.com/india/products/hana.html#:~:text=is%20SAP%20HANA-,in%2Dmemory,-database%3F

質問 # 160
You are managing several Google Cloud Platform (GCP) projects and need access to all logs for the past 60 days. You want to be able to explore and quickly analyze the log contents. You want to follow Google- recommended practices to obtain the combined logs for all projects. What should you do?

A. Navigate to Stackdriver Logging and select resource.labels.project_id="*"
B. Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days.
C. Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days.
D. Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days.

正解：B

解説：
* Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right.
Log entries are held in Stackdriver Logging for a limited time known as the retention period which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks.
Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging
* Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right.
While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
* Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.Ref: https://cloud.
google.com/logging/docs/export/configure_export_v2
Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.Ref: https://cloud.google.com/logging/docs/export
/aggregated_sinks
Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage.
* Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.Ref: https://cloud.
google.com/logging/docs/export/configure_export_v2
Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.Ref: https://cloud.google.com/logging/docs/export
/aggregated_sinks
Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage.
Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.Ref: https://cloud.google.com/bigquery/docs/best-practices- storage

質問 # 161
Every employee of your company has a Google account. Your operational team needs to manage a large number of instances on Compute Engine. Each member of this team needs only administrative access to the servers. Your security team wants to ensure that the deployment of credentials is operationally efficient and must be able to determine who accessed a given instance. What should you do?

A. Ask each member of the team to generate a new SSH key pair and to send you their public key.
Use a configuration management tool to deploy those keys on each instance.
B. Generate a new SSH key pair. Give the private key to each member of your team.
Configure the public key as a project-wide public SSH key in your Cloud Platform project and allow project-wide public SSH keys on each instance.
C. Generate a new SSH key pair. Give the private key to each member of your team.
Configure the public key in the metadata of each instance.
D. Ask each member of the team to generate a new SSH key pair and to add the public key to their Google account.
Grant the "compute.osAdminLogin" role to the Google group corresponding to this team.

正解：B

質問 # 162
You are building a pipeline to process time-series data. Which Google Cloud Platform services should you put in boxes 1,2,3, and 4?

A. Cloud Pub/Sub, Cloud Dataflow, Cloud Datastore, BigQuery
B. Cloud Pub/Sub, Cloud Storage, BigQuery, Cloud Bigtable
C. Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable, BigQuery
D. Firebase Messages, Cloud Pub/Sub, Cloud Spanner, BigQuery

正解：C

解説：
Reference:
https://cloud.google.com/solutions/correlating-time-series-dataflow

質問 # 163
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