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Google Associate-Cloud-Engineer Exam Syllabus Topics:

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

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Google Associate Cloud Engineer Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
You are setting up a Windows VM on Compute Engine and want to make sure you can log in to the VM via RDP. What should you do?

A. After the VM has been created, use gcloud compute reset-windows-password to retrieve the login credentials for the VM.
B. After the VM has been created, download the JSON private key for the default Compute Engine service account. Use the credentials in the JSON file to log in to the VM.
C. After the VM has been created, use your Google Account credentials to log in into the VM.
D. When creating the VM, add metadata to the instance using `windows-password' as the key and a password as the value.

Answer: A

Explanation:
If the Windows account does not exist, this command will cause the account to be created and the password for that new account will be returned.
https://cloud.google.com/sdk/gcloud/reference/beta/compute/reset-windows-password

NEW QUESTION # 53
You have a number of applications that have bursty workloads and are heavily dependent on topics to decouple publishing systems from consuming systems. Your company would like to go serverless to enable developers to focus on writing code without worrying about infrastructure. Your solution architect has already identified Cloud Pub/Sub as a suitable alternative for decoupling systems. You have been asked to identify a suitable GCP Serverless service that is easy to use with Cloud Pub/Sub. You want the ability to scale down to zero when there is no traffic in order to minimize costs. You want to follow Google recommended practices.
What should you suggest?

A. App Engine Standard
B. Cloud Run for Anthos
C. Cloud Run
D. Cloud Functions.

Answer: D

Explanation:
Cloud Functions is Google Cloud's event-driven serverless compute platform that lets you run your code locally or in the cloud without having to provision servers. Cloud Functions scales up or down, so you pay only for compute resources you use. Cloud Functions have excellent integration with Cloud Pub/Sub, lets you scale down to zero and is recommended by Google as the ideal serverless platform to use when dependent on Cloud Pub/Sub."If you're building a simple API (a small set of functions to be accessed via HTTP or Cloud Pub/Sub), we recommend using Cloud Functions."Ref: https://cloud.google.com/serverless-options

NEW QUESTION # 54
You are setting up a Windows VM on Compute Engine and want to make sure you can log in to the VM via RDP. What should you do?

A. After the VM has been created, use gcloud compute reset-windows-password to retrieve the login credentials for the VM.
B. After the VM has been created, download the JSON private key for the default Compute Engine service account. Use the credentials in the JSON file to log in to the VM.
C. After the VM has been created, use your Google Account credentials to log in into the VM.
D. When creating the VM, add metadata to the instance using 'windows-password' as the key and a password as the value.

Answer: A

Explanation:
You can generate Windows passwords using either the Google Cloud Console or the gcloud command-line tool. This option uses the right syntax to reset the windows password.
gcloud compute reset-windows-password windows-instance
Ref: https://cloud.google.com/compute/docs/instances/windows/creating-passwords-for-windows-instances#gcloud

NEW QUESTION # 55
You are running multiple VPC-native Google Kubernetes Engine clusters in the same subnet. The IPs available for the nodes are exhausted, and you want to ensure that the clusters can grow in nodes when needed.
What should you do?

A. Expand the CIDR range of the relevant subnet for the cluster.
B. Create a new VPC, and set up VPC peering with the existing VPC.
C. Create a new subnet in the same region as the subnet being used.
D. Add an alias IP range to the subnet used by the GKE clusters.

Answer: A

Explanation:
Explanation
gcloud compute networks subnets expand-ip-range NAME gcloud compute networks subnets expand-ip-range
- expand the IP range of a Compute Engine subnetwork
https://cloud.google.com/sdk/gcloud/reference/compute/networks/subnets/expand-ip-range

NEW QUESTION # 56
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?

A. The pending Pod's resource requests are too large to fit on a single node of the cluster.
B. The node pool is configured with a service account that does not have permission to pull the container image used by the pending Pod.
C. Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod.
D. The pending Pod was originally scheduled on a node that has been preempted between the creation of the Deployment and your verification of the Pods' status. It is currently being rescheduled on a new node.

Answer: C

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 # 57
......

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