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Google Associate-Cloud-Engineer Exam Syllabus Topics:

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

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Google Associate Cloud Engineer Exam Sample Questions (Q288-Q293):

NEW QUESTION # 288
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. Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days.
B. Navigate to Stackdriver Logging and select resource.labels.project_id="*"
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.

Answer: A

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

NEW QUESTION # 289
You are planning to migrate a database and a backend application to a Standard Google Kubernetes Engine (GKE) cluster. You need to prevent data loss and make sure there are enough nodes available for your backend application based on the demands of your workloads.
You want to follow Google- recommended practices and minimize the amount of manual work required. What should you do?

A. Run your database as a single Pod. Run the resize command when you notice changes in the demands of your workloads.
B. Run your database as a DaemonSet. Run the resize command when you notice changes in the demands of your workloads.
C. Run your database as a Deployment. Configure cluster autoscaling to handle changes in the demands of your workloads.
D. Run your database as a StatefulSet. Configure cluster autoscaling to handle changes in the demands of your workloads.

Answer: D

Explanation:
StatefulSet for the Database:
Databases are stateful applications that require persistent storage and stable network identities.
A StatefulSet is designed specifically for stateful applications, as it provides unique, stable network identifiers and persistent storage via PersistentVolumeClaims (PVCs). This setup helps ensure that data is not lost during pod rescheduling or node failures.
Cluster Autoscaling for the Backend Application:
Configuring cluster autoscaling allows the GKE cluster to automatically add or remove nodes based on the demands of your workloads. This ensures that the backend application has sufficient compute resources available to handle fluctuating workloads, while minimizing the need for manual intervention.

NEW QUESTION # 290
You have an application on a general-purpose Compute Engine instance that is experiencing excessive disk read throttling on its Zonal SSD Persistent Disk. The application primarily reads large files from disk. The disk size is currently 350 GB. You want to provide the maximum amount of throughput while minimizing costs. What should you do?

A. Increase the size of the disk to 1 TB.
B. Migrate to use a Regional SSD on the instance.
C. Migrate to use a Local SSD on the instance.
D. Increase the allocated CPU to the instance.

Answer: C

Explanation:
https://cloud.google.com/compute/docs/disks/performance

NEW QUESTION # 291
You have a Google Cloud Platform account with access to both production and development projects. You need to create an automated process to list all compute instances in development and production projects on a daily basis. What should you do?

A. Go to Cloud Shell and export this information to Cloud Storage on a daily basis.
B. Go to GCP Console and export this information to Cloud SQL on a daily basis.
C. Create two configurations using gcloud config. Write a script that sets configurations as active, individually. For each configuration, use gcloud compute instances list to get a list of compute resources.
D. Create two configurations using gsutil config. Write a script that sets configurations as active, individually. For each configuration, use gsutil compute instances list to get a list of compute resources.

Answer: C

Explanation:
You can create two configurations - one for the development project and another for the production project.
And you do that by running "gcloud config configurations create" command.https://cloud.google.com/sdk
/gcloud/reference/config/configurations/createIn your custom script, you can load these configurations one at a time and execute gcloud compute instances list to list Google Compute Engine instances in the project that is active in the gcloud configuration.Ref: https://cloud.google.com/sdk/gcloud/reference/compute/instances
/listOnce you have this information, you can export it in a suitable format to a suitable target e.g. export as CSV or export to Cloud Storage/BigQuery/SQL, etc

NEW QUESTION # 292
You have deployed an application on a Compute Engine instance. An external consultant needs to access the Linux-based instance. The consultant is connected to your corporate network through a VPN connection, but the consultant has no Google account. What should you do?

A. Instruct the external consultant to use the gcloud compute ssh command line tool by using the public IP address of the instance to access it.
B. Instruct the external consultant to use the gcloud compute ssh command line tool by using Identity-Aware Proxy to access the instance.
C. Instruct the external consultant to generate an SSH key pair, and request the private key from the consultant. Add the private key to the instance yourself, and have the consultant access the instance through SSH with their public key.
D. Instruct the external consultant to generate an SSH key pair, and request the public key from the consultant. Add the public key to the instance yourself, and have the consultant access the instance through SSH with their private key.

Answer: D

Explanation:
The best option is to instruct the external consultant to generate an SSH key pair, and request the public key from the consultant. Then, add the public key to the instance yourself, and have the consultant access the instance through SSH with their private key. This way, you can grant the consultant access to the instance without requiring a Google account or exposing the instance's public IP address. This option also follows the best practice of using user-managed SSH keys instead of service account keys for SSH access1.
Option A is not feasible because the external consultant does not have a Google account, and therefore cannot use Identity-Aware Proxy (IAP) to access the instance. IAP requires the user to authenticate with a Google account and have the appropriate IAM permissions to access the instance2. Option B is not secure because it exposes the instance's public IP address, which can increase the risk of unauthorized access or attacks. Option D is not correct because it reverses the roles of the public and private keys. The public key should be added to the instance, and the private key should be kept by the consultant. Sharing the private key with anyone else can compromise the security of the SSH connection3.
References:
* 1: https://cloud.google.com/compute/docs/instances/adding-removing-ssh-keys
* 2: https://cloud.google.com/iap/docs/using-tcp-forwarding
* 3: https://cloud.google.com/compute/docs/instances/connecting-advanced#sshbetweeninstances

NEW QUESTION # 293
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