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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Setting Up a Cloud Solution Environment	20-25%	- Managing billing configuration 1. Setting up budget alerts and notifications 2. Linking billing to projects 3. Creating billing accounts - Setting up cloud projects and accounts 1. Assigning IAM roles to users and groups 2. Managing billing budgets and alerts 3. Creating and managing projects
Topic 2: Configuring Access and Security	15-20%	- Managing IAM resources 1. Defining custom IAM roles 2. Creating and managing 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
Topic 3: 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 configuring compute resources 1. Configuring persistent disks and local SSDs 2. Selecting appropriate machine types and zones - Planning and configuring data storage options 1. Selecting appropriate storage classes 2. Choosing between Cloud Storage, Filestore, Persistent Disk - Planning and estimating GCP product usage 1. Reading documentation for compute, storage, and network resources 2. Using GCP Pricing Calculator
Topic 4: Deploying and Implementing a Cloud Solution	20-25%	- Deploying and managing Compute Engine resources 1. Creating and managing instances with startup scripts 2. Creating and managing custom machine types 3. Creating and managing managed instance groups - Deploying and managing Kubernetes Engine resources 1. Configuring kubectl for cluster access 2. Deploying workloads with Deployments and Services 3. Creating and managing GKE clusters - Deploying and managing Cloud Run resources 1. Configuring memory limits and scaling settings 2. Deploying containerized applications to Cloud Run - 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
Topic 5: Ensuring Successful Operation	15-20%	- Managing Compute Engine resources 1. Attaching and detaching disks 2. Resizing instances and persistent disks 3. Managing instances, disks, and snapshots - Managing Cloud Run resources 1. Configuring resource limits 2. Managing revisions and traffic splitting - Managing Kubernetes Engine resources 1. Scaling clusters and node pools 2. Upgrading clusters 3. Managing workloads (Deployments, Services, HPA) - Managing storage and data solutions 1. Managing Cloud Storage buckets and objects 2. Configuring CORS and encryption 3. Setting lifecycle policies - Managing logging and monitoring 1. Using Cloud Monitoring and Logging 2. Viewing and analyzing logs 3. Creating metrics and alerts

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Google Associate Cloud Engineer Exam Sample Questions (Q194-Q199):

NEW QUESTION # 194
Your company is moving from an on-premises environment to Google Cloud Platform (GCP). You have multiple development teams that use Cassandra environments as backend databases. They all need a development environment that is isolated from other Cassandra instances. You want to move to GCP quickly and with minimal support effort. What should you do?

A. 1. Advise your developers to go to Cloud Marketplace.
2. Ask the developers to launch a Cassandra image for their development work.
B. 1. Build a Cassandra Compute Engine instance and take a snapshot of it.
2. Use the snapshot to create instances for your developers.
C. 1. Build a Cassandra Compute Engine instance and take a snapshot of it.
2.Upload the snapshot to Cloud Storage and make it accessible to your developers.
3.Build instructions to create a Compute Engine instance from the snapshot so that developers can do it themselves.
D. 1. Build an instruction guide to install Cassandra on GCP.
2. Make the instruction guide accessible to your developers.

Answer: C

NEW QUESTION # 195
Your company runs its Linux workloads on Compute Engine instances. Your company will be working with a new operations partner that does not use Google Accounts. You need to grant access to the instances to your operations partner so they can maintain the installed tooling. What should you do?

A. Enable Cloud IAP for the Compute Engine instances, and add the operations partner as a Cloud IAP Tunnel User.
B. Tag all the instances with the same network tag. Create a firewall rule in the VPC to grant TCP access on port 22 for traffic from the operations partner to instances with the network tag.
C. Ask the operations partner to generate SSH key pairs, and add the public keys to the VM instances.
D. Set up Cloud VPN between your Google Cloud VPC and the internal network of the operations partner.

Answer: C

Explanation:
Explanation
IAP controls access to your App Engine apps and Compute Engine VMs running on Google Cloud. It leverages user identity and the context of a request to determine if a user should be allowed access. IAP is a building block toward BeyondCorp, an enterprise security model that enables employees to work from untrusted networks without using a VPN.
By default, IAP uses Google identities and IAM. By leveraging Identity Platform instead, you can authenticate users with a wide range of external identity providers, such as:
Email/password
OAuth (Google, Facebook, Twitter, GitHub, Microsoft, etc.)
SAML
OIDC
Phone number
Custom
Anonymous
This is useful if your application is already using an external authentication system, and migrating your users to Google accounts is impractical.
https://cloud.google.com/iap/docs/using-tcp-forwarding#grant-permission

NEW QUESTION # 196
You want to select and configure a cost-effective solution for relational data on Google Cloud Platform. You are working with a small set of operational data in one geographic location. You need to support point- in-time recovery. What should you do?

A. Select Cloud Spanner. Set up your instance as multi-regional.
B. Select Cloud SQL (MySQL). Select the create failover replicas option.
C. Select Cloud Spanner. Set up your instance with 2 nodes.
D. Select Cloud SQL (MySQL). Verify that the enable binary logging option is selected.

Answer: D

Explanation:
You must enable binary logging to use point-in-time recovery. Enabling binary logging causes a slight reduction in write performance.
https://cloud.google.com/sql/docs/mysql/backup-recovery/backups

NEW QUESTION # 197
Your company uses Cloud Storage to store application backup files for disaster recovery purposes. You want to follow Google's recommended practices. Which storage option should you use?

A. Regional Storage
B. Multi-Regional Storage
C. Coldline Storage
D. Nearline Storage

Answer: C

Explanation:
Reference:
https://cloud.google.com/storage/docs/storage-classes#nearline

NEW QUESTION # 198
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. Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod.
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. 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.
D. The pending Pod's resource requests are too large to fit on a single node of the cluster.

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 # 199
......

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