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The Google Associate Cloud Engineer Exam (Associate-Cloud-Engineer) certification is a valuable credential that every Google professional should earn it. The Google Associate-Cloud-Engineer certification exam offers a great opportunity for beginners and experienced professionals to demonstrate their expertise. With the Google Associate Cloud Engineer Exam (Associate-Cloud-Engineer) certification exam everyone can upgrade their skills and knowledge. There are other several benefits that the Associate-Cloud-Engineer Exam holders can achieve after the success of the Google Associate Cloud Engineer Exam (Associate-Cloud-Engineer) certification exam. However, you should keep in mind to pass the Google Associate-Cloud-Engineer certification exam is not an easy task. It is a challenging job.

Google Associate-Cloud-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Planning and configuring a cloud solution17.5%- 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. Plan instance configuration
  • 3. Select compute options
  • 4. Plan data storage solutions
- Planning and configuring storage resources
  • 1. Select storage classes
  • 2. Plan data durability and availability
Topic 2: Configuring access and security17.5%- Configuring network security
  • 1. Manage security controls
  • 2. Configure private access
  • 3. Secure VPC networks
- Managing IAM
  • 1. Manage service accounts
  • 2. Define roles and permissions
  • 3. Implement least privilege access
- Managing data security
  • 1. Enable encryption
  • 2. Manage access to data
Topic 3: Ensuring successful operation of a cloud solution20%- Monitoring and logging
  • 1. Use Cloud Monitoring
  • 2. Use Cloud Logging
  • 3. Set up alerts
- 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: Deploying and implementing a cloud solution25%- Deploying and implementing Google Kubernetes Engine resources
  • 1. Create and manage clusters
  • 2. Configure networking for GKE
  • 3. Deploy workloads
- Deploying and implementing data solutions
  • 1. Set up data processing pipelines
  • 2. Provision databases
- Deploying and implementing serverless resources
  • 1. Create Cloud Functions
  • 2. Deploy Cloud Run services
- Deploying and implementing Compute Engine resources
  • 1. Manage images and snapshots
  • 2. Launch virtual machines
  • 3. Configure access and security
  • 4. Create managed instance groups
Topic 5: Setting up a cloud solution environment20%- Setting up cloud projects and accounts
  • 1. Manage quotas and limits
  • 2. Manage IAM roles and permissions
  • 3. Apply organizational policies
  • 4. Enable APIs
  • 5. Create resource hierarchy
  • 6. Manage users and groups
- Managing billing configuration
  • 1. Set budgets and alerts
  • 2. Link projects to billing accounts
  • 3. Configure billing exports
  • 4. Create and manage billing accounts

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Google Associate Cloud Engineer Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
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?

Answer: A

Explanation:
You can use the Google Cloud Pricing Calculator to total the estimated monthly costs for each GCP product. You dont incur any charges for doing so.
Ref: https://cloud.google.com/products/calculator


NEW QUESTION # 16
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?

Answer: B

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


NEW QUESTION # 17
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?

Answer: A

Explanation:
"Billing Account Viewer access would usually be granted to finance teams, it provides access to spend information, but does not confer the right to link or unlink projects or otherwise manage the properties of the billing account." https://cloud.google.com/billing/docs/how-to/billing-access


NEW QUESTION # 18
You create a Deployment with 2 replicas in a Google Kubernetes Engine cluster that has a single preemptible node pool. After a few minutes, you use kubectl to examine the status of your Pod and observe that one of them is still in Pending status:

What is the most likely cause?

Answer: A

Explanation:
The pending Pods resource requests are too large to fit on a single node of the cluster. Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod. is the right answer.
When you have a deployment with some pods in running and other pods in the pending state, more often than not it is a problem with resources on the nodes. Heres a sample output of this use case. We see that the problem is with insufficient CPU on the Kubernetes nodes so we have to either enable auto-scaling or manually scale up the nodes.


NEW QUESTION # 19
You are configuring service accounts for an application that spans multiple projects. Virtual machines (VMs) running in the web-applications project need access to BigQuery datasets in crm-databases-proj. You want to follow Google-recommended practices to give access to the service account in the web-applications project. What should you do?

Answer: C

Explanation:
Reference:
bigquery.dataViewer role provides permissions to read the datasets metadata and list tables in the dataset as well as Read data and metadata from the datasets tables. This is exactly what we need to fulfil this requirement and follows the least privilege principle.
Ref: https://cloud.google.com/iam/docs/understanding-roles#bigquery-roles


NEW QUESTION # 20
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