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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring Successful Operation	15-20%	- Managing Compute Engine resources 1. Attaching and detaching disks 2. Managing instances, disks, and snapshots 3. Resizing instances and persistent disks - Managing Kubernetes Engine resources 1. Upgrading clusters 2. Managing workloads (Deployments, Services, HPA) 3. Scaling clusters and node pools - Managing storage and data solutions 1. Managing Cloud Storage buckets and objects 2. Setting lifecycle policies 3. Configuring CORS and encryption - Managing logging and monitoring 1. Viewing and analyzing logs 2. Creating metrics and alerts 3. Using Cloud Monitoring and Logging - Managing Cloud Run resources 1. Configuring resource limits 2. Managing revisions and traffic splitting
Configuring Access and Security	15-20%	- Viewing audit logs 1. Configuring audit log policies 2. Accessing and analyzing Cloud Audit Logs - Managing service accounts 1. Assigning service accounts to resources 2. Using service account keys 3. Creating and managing service accounts - Managing IAM resources 1. Defining custom IAM roles 2. Applying IAM policies to resources 3. Creating and managing IAM roles
Setting Up a Cloud Solution Environment	20-25%	- Managing billing configuration 1. Creating billing accounts 2. Setting up budget alerts and notifications 3. Linking billing to projects - Setting up cloud projects and accounts 1. Creating and managing projects 2. Managing billing budgets and alerts 3. Assigning IAM roles to users and groups
Planning and Configuring a Cloud Solution	20-25%	- Planning and configuring data storage options 1. Selecting appropriate storage classes 2. Choosing between Cloud Storage, Filestore, Persistent Disk - 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. Configuring load balancing and firewall rules 2. Designing VPC networks - Planning and estimating GCP product usage 1. Reading documentation for compute, storage, and network resources 2. Using GCP Pricing Calculator
Deploying and Implementing a Cloud Solution	20-25%	- Deploying and managing Kubernetes Engine resources 1. Creating and managing GKE clusters 2. Deploying workloads with Deployments and Services 3. Configuring kubectl for cluster access - Deploying applications with Cloud Deployment Manager 1. Using templates for resource provisioning 2. Creating deployment configurations - Deploying and managing Cloud Functions 1. Creating and triggering Cloud Functions 2. Configuring triggers and environment variables - Deploying and managing Compute Engine resources 1. Creating and managing managed instance groups 2. Creating and managing instances with startup scripts 3. Creating and managing custom machine types - Deploying and managing Cloud Run resources 1. Deploying containerized applications to Cloud Run 2. Configuring memory limits and scaling settings

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Google Associate Cloud Engineer Exam Sample Questions (Q353-Q358):

NEW QUESTION # 353
Your company's accounting department needs to run an overnight batch workload every day. You must implement a solution that minimizes the cost to run this workload and automatically retries the batch if an execution fails. What should you do?

A. Develop an application that runs the batch workload, and deploy the application as a Google Kubernetes Engine (GKE) CronJob.
B. Develop a web application that listens for incoming requests, and deploy the application as a Cloud Run service. Use Cloud Scheduler to call the HTTP endpoint.
C. Develop a web application that listens for incoming requests, and deploy the application as a Google Kubernetes Engine (GKE) Deployment. Use Cloud Scheduler to call the HTTP endpoint.
D. Develop an application that runs the batch workload, and deploy the application as a Cloud Run job. Use Cloud Scheduler to trigger the job.

Answer: D

Explanation:
Developing an application that runs the batch workload and deploying it as a Cloud Run job is the best solution. Cloud Run jobs are serverless, cost-efficient, and automatically scale based on demand, allowing you to pay only for the resources used during execution. You can also use Cloud Scheduler to trigger the job on a schedule. Additionally, Cloud Run supports automatic retries, which meets the requirement for retrying the batch if an execution fails.

NEW QUESTION # 354
Your team maintains the infrastructure for your organization. The current infrastructure requires changes. You need to share your proposed changes with the rest of the team. You want to follow Google's recommended best practices. What should you do?

A. Use Deployment Manager templates to describe the proposed changes and store them in a Cloud Storage bucket.
B. Apply the change in a development environment, run gcloud compute instances list, and then save the output in a shared Storage bucket.
C. Use Deployment Manager templates to describe the proposed changes and store them in Cloud Source Repositories.
D. Apply the change in a development environment, run gcloud compute instances list, and then save the output in Cloud Source Repositories.

Answer: C

NEW QUESTION # 355
You are building an application that stores relational data from users. Users across the globe will use this application. Your CTO is concerned about the scaling requirements because the size of the user base is unknown. You need to implement a database solution that can scale with your user growth with minimum configuration changes. Which storage solution should you use?

A. Cloud Spanner
B. Cloud SQL
C. Cloud Datastore
D. Cloud Firestore

Answer: A

Explanation:
CloudSpanner supports relational data model, globaly. Option D is incorrect, Cloud Datastore is to store NonRelational data.

NEW QUESTION # 356
Your development team has asked you to set up an external TCP load balancer with SSL offload. Which load balancer should you use?

A. SSL proxy
B. HTTP load balancer
C. HTTPS load balancer
D. TCP proxy

Answer: A,C

NEW QUESTION # 357
You have a virtual machine that is currently configured with 2 vCPUs and 4 GB of memory. It is running out of memory. You want to upgrade the virtual machine to have 8 GB of memory. What should you do?

A. Use gcloud to add metadata to the VM. Set the key to required-memory-size and the value to 8 GB.
B. Stop the VM, increase the memory to 8 GB, and start the VM.
C. Stop the VM, change the machine type to n1-standard-8, and start the VM.
D. Rely on live migration to move the workload to a machine with more memory.

Answer: B

Explanation:
In Google compute engine, if predefined machine types don't meet your needs, you can create an instance with custom virtualized hardware settings. Specifically, you can create an instance with a custom number of vCPUs and custom memory, effectively using a custom machine type. Custom machine types are ideal for the following scenarios: 1. Workloads that aren't a good fit for the predefined machine types that are available to you. 2. Workloads that require more processing power or more memory but don't need all of the upgrades that are provided by the next machine type level. In our scenario, we only need a memory upgrade. Moving to a bigger instance would also bump up the CPU which we don't need so we have to use a custom machine type. It is not possible to change memory while the instance is running so you need to first stop the instance, change the memory and then start it again. See below a screenshot that shows how CPU/Memory can be customized for an instance that has been stopped. Ref: https://cloud.google.com/compute/docs/instances/creating-instance-with-custom-machine-type

NEW QUESTION # 358
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