Google Associate-Cloud-Engineer - Google Cloud Certified - Associate Cloud Engineer

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Total 363 questions

Your company is using Google Workspace to manage employee accounts. Anticipated growth will increase the number of personnel from 100 employees to 1.000 employees within 2 years. Most employees will need access to your company ' s Google Cloud account. The systems and processes will need to support 10x growth without performance degradation, unnecessary complexity, or security issues. What should you do?

Migrate the users to Active Directory. Connect the Human Resources system to Active Directory. Turn on Google Cloud Directory Sync (GCDS) for Cloud Identity. Turn on Identity Federation from Cloud Identity to Active Directory.

Organize the users in Cloud Identity into groups. Enforce multi-factor authentication in Cloud Identity.

Turn on identity federation between Cloud Identity and Google Workspace. Enforce multi-factor authentication for domain wide delegation.

Use a third-party identity provider service through federation. Synchronize the users from Google Workplace to the third-party provider in real time.

Question # 92

You want to configure an SSH connection to a single Compute Engine instance for users in the dev1 group. This instance is the only resource in this particular Google Cloud Platform project that the dev1 users should be able to connect to. What should you do?

Set metadata to enable-oslogin=true for the instance. Grant the dev1 group the compute.osLogin role. Direct them to use the Cloud Shell to ssh to that instance.

Set metadata to enable-oslogin=true for the instance. Set the service account to no service account for that instance. Direct them to use the Cloud Shell to ssh to that instance.

Enable block project wide keys for the instance. Generate an SSH key for each user in the dev1 group. Distribute the keys to dev1 users and direct them to use their third-party tools to connect.

Enable block project wide keys for the instance. Generate an SSH key and associate the key with that instance. Distribute the key to dev1 users and direct them to use their third-party tools to connect.

Question # 93

Youâ€™ve deployed a microservice called myapp1 to a Google Kubernetes Engine cluster using the YAML file specified below:

You need to refactor this configuration so that the database password is not stored in plain text. You want to follow Google-recommended practices. What should you do?

Store the database password inside the Docker image of the container, not in the YAML file.

Store the database password inside a Secret object. Modify the YAML file to populate the DB_PASSWORD environment variable from the Secret.

Store the database password inside a ConfigMap object. Modify the YAML file to populate the DB_PASSWORD environment variable from the ConfigMap.

Store the database password in a file inside a Kubernetes persistent volume, and use a persistent volume claim to mount the volume to the container.

Question # 94

You created a Google Cloud Platform project with an App Engine application inside the project. You initially configured the application to be served from the us-central region. Now you want the application to be served from the asia-northeast1 region. What should you do?

Change the default region property setting in the existing GCP project to asia-northeast1.

Change the region property setting in the existing App Engine application from us-central to asia-northeast1.

Create a second App Engine application in the existing GCP project and specify asia-northeast1 as the region to serve your application.

Create a new GCP project and create an App Engine application inside this new project. Specify asia-northeast1 as the region to serve your application.

Question # 95

You need to monitor resources that are distributed over different projects in Google Cloud Platform. You want to consolidate reporting under the same Stackdriver Monitoring dashboard. What should you do?

Use Shared VPC to connect all projects, and link Stackdriver to one of the projects.

For each project, create a Stackdriver account. In each project, create a service account for that project and grant it the role of Stackdriver Account Editor in all other projects.

Configure a single Stackdriver account, and link all projects to the same account.

Configure a single Stackdriver account for one of the projects. In Stackdriver, create a Group and add the other project names as criteria for that Group.

Question # 96

You are planning to migrate the following on-premises data management solutions to Google Cloud:

â€¢ One MySQL cluster for your main database

â€¢ Apache Kafka for your event streaming platform

â€¢ One Cloud SOL for PostgreSOL database for your analytical and reporting needs

You want to implement Google-recommended solutions for the migration. You need to ensure that the new solutions provide global scalability and require minimal operational and infrastructure management. What should you do?

Migrate from MySQL to Cloud SQL, from Kafka to Memorystore, and from Cloud SQL for PostgreSQL to Cloud SQL

Migrate from MySQL to Cloud Spanner, from Kafka to Memorystore, and from Cloud SOL for PostgreSQL to Cloud SQL

Migrate from MySQL to Cloud SOL, from Kafka to Pub/Sub, and from Cloud SOL for PostgreSQL to BigQuery.

Migrate from MySQL to Cloud Spanner, from Kafka to Pub/Sub. and from Cloud SQL for PostgreSQL to BigQuery

Question # 97

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?

Navigate to Stackdriver Logging and select resource.labels.project_id= " * "

Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days.

Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days.

Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days.

Explanation:

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 periodÂ 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:Â 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:Â 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:Â 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:Â 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:Â https://cloud.google.com/logging/docs/export/configure_export_v2

Ref:Â 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:Â https://cloud.google.com/bigquery/docs/best-practices-storage

[Reference: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit- logging, , ]

Question # 98

Your finance team wants to view the billing report for your projects. You want to make sure that the finance team does not get additional permissions to the project. What should you do?

Add the group for the finance team to roles/billing user role.

Add the group for the finance team to roles/billing admin role.

Add the group for the finance team to roles/billing viewer role.

Add the group for the finance team to roles/billing project/Manager role.

Question # 99

You have an on-premises data analytics set of binaries that processes data files in memory for about 45 minutes every midnight. The sizes of those data files range from 1 gigabyte to 16 gigabytes. You want to migrate this application to Google Cloud with minimal effort and cost. What should you do?

Upload the code to Cloud Functions. Use Cloud Scheduler to start the application.

Create a container for the set of binaries. Use Cloud Scheduler to start a Cloud Run job for the container.

Create a container for the set of binaries Deploy the container to Google Kubernetes Engine (GKE) and use the Kubernetes scheduler to start the application.

Lift and shift to a VM on Compute Engine. Use an instance schedule to start and stop the instance.

Question # 100

You are building an application that processes data files uploaded from thousands of suppliers. Your primary goals for the application are data security and the expiration of aged data. You need to design the application to:

â€¢Restrict access so that suppliers can access only their own data.

â€¢Give suppliers write access to data only for 30 minutes.

â€¢Delete data that is over 45 days old.

You have a very short development cycle, and you need to make sure that the application requires minimal maintenance. Which two strategies should you use? (Choose two.)

Build a lifecycle policy to delete Cloud Storage objects after 45 days.

Use signed URLs to allow suppliers limited time access to store their objects.

Set up an SFTP server for your application, and create a separate user for each supplier.

Build a Cloud function that triggers a timer of 45 days to delete objects that have expired.

Develop a script that loops through all Cloud Storage buckets and deletes any buckets that are older than 45 days.

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Google Associate-Cloud-Engineer - Google Cloud Certified - Associate Cloud Engineer

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