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Google Associate-Cloud-Engineer Exam Syllabus Topics:

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

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Google Associate Cloud Engineer Exam Sample Questions (Q165-Q170):

NEW QUESTION # 165
You are using Deployment Manager to create a Google Kubernetes Engine cluster. Using the same Deployment Manager deployment, you also want to create a DaemonSet in the kube- system namespace of the cluster. You want a solution that uses the fewest possible services.
What should you do?

A. Use the Deployment Manager Runtime Configurator to create a new Config resource that contains the DaemonSet definition.
B. In the cluster's definition in Deployment Manager, add a metadata that has kube-system as key and the DaemonSet manifest as value.
C. Add the cluster's API as a new Type Provider in Deployment Manager, and use the new type to create the DaemonSet.
D. With Deployment Manager, create a Compute Engine instance with a startup script that uses kubectl to create the DaemonSet.

Answer: C

Explanation:
Adding an API as a type provider
This page describes how to add an API to Google Cloud Deployment Manager as a type provider. To learn more about types and type providers, read the Types overview documentation.
A type provider exposes all of the resources of a third-party API to Deployment Manager as base types that you can use in your configurations. These types must be directly served by a RESTful API that supports Create, Read, Update, and Delete (CRUD).
If you want to use an API that is not automatically provided by Google with Deployment Manager, you must add the API as a type provider.
https://cloud.google.com/deployment-manager/docs/configuration/type-providers/creating-type- provider

NEW QUESTION # 166
You create a new Google Kubernetes Engine (GKE) cluster and want to make sure that it always runs a supported and stable version of Kubernetes. What should you do?

A. Select the latest available cluster version for your GKE cluster.
B. Enable the Node Auto-Repair feature for your GKE cluster.
C. Select "Container-Optimized OS (cos)" as a node image for your GKE cluster.
D. Enable the Node Auto-Upgrades feature for your GKE cluster.

Answer: D

NEW QUESTION # 167
You need to assign a Cloud Identity and Access Management (Cloud IAM) role to an external auditor. The auditor needs to have permissions to review your Google Cloud Platform (GCP) Audit Logs and also to review your Data Access logs. What should you do?

A. Assign the auditor the IAM role roles/logging.privateLogViewer. Perform the export of logs to Cloud Storage.
B. Assign the auditor's IAM user to a custom role that has logging.privateLogEntries.list permission. Perform the export of logs to Cloud Storage.
C. Assign the auditor's IAM user to a custom role that has logging.privateLogEntries.list permission. Direct the auditor to also review the logs for changes to Cloud IAM policy.
D. Assign the auditor the IAM role roles/logging.privateLogViewer. Direct the auditor to also review the logs for changes to Cloud IAM policy.

Answer: B

NEW QUESTION # 168
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 # 169
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

NEW QUESTION # 170
......

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