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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring Successful Operation	15-20%	- Managing storage and data solutions 1. Configuring CORS and encryption 2. Managing Cloud Storage buckets and objects 3. Setting lifecycle policies - Managing Kubernetes Engine resources 1. Managing workloads (Deployments, Services, HPA) 2. Upgrading clusters 3. Scaling clusters and node pools - Managing Compute Engine resources 1. Resizing instances and persistent disks 2. Attaching and detaching disks 3. Managing instances, disks, and snapshots - Managing Cloud Run resources 1. Configuring resource limits 2. Managing revisions and traffic splitting - Managing logging and monitoring 1. Creating metrics and alerts 2. Viewing and analyzing logs 3. Using Cloud Monitoring and Logging
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. Managing billing budgets and alerts 2. Creating and managing projects 3. Assigning IAM roles to users and groups
Deploying and Implementing a Cloud Solution	20-25%	- Deploying and managing Cloud Run resources 1. Deploying containerized applications to Cloud Run 2. Configuring memory limits and scaling settings - 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 applications with Cloud Deployment Manager 1. Creating deployment configurations 2. Using templates for resource provisioning - Deploying and managing Cloud Functions 1. Creating and triggering Cloud Functions 2. Configuring triggers and environment variables - Deploying and managing Kubernetes Engine resources 1. Configuring kubectl for cluster access 2. Creating and managing GKE clusters 3. Deploying workloads with Deployments and Services
Configuring Access and Security	15-20%	- Managing service accounts 1. Assigning service accounts to resources 2. Using service account keys 3. Creating and managing service accounts - Managing IAM resources 1. Creating and managing IAM roles 2. Defining custom IAM roles 3. Applying IAM policies to resources - Viewing audit logs 1. Accessing and analyzing Cloud Audit Logs 2. Configuring audit log policies
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. Reading documentation for compute, storage, and network resources 2. Using GCP Pricing Calculator - 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

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Associate-Cloud-Engineer Übungsfragen: Google Associate Cloud Engineer Exam & Associate-Cloud-Engineer Dateien Prüfungsunterlagen

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Google Associate Cloud Engineer Exam Associate-Cloud-Engineer Prüfungsfragen mit Lösungen (Q250-Q255):

250. Frage
You are using Data Studio to visualize a table from your data warehouse that is built on top of BigQuery. Data is appended to the data warehouse during the day. At night, the daily summary is recalculated by overwriting the table. You just noticed that the charts in Data Studio are broken, and you want to analyze the problem.
What should you do?

A. In Cloud Logging create a filter for your Data Studio report
B. Use the open source CLI tool. Snapshot Debugger, to find out why the data was not refreshed correctly.
C. Use the BigQuery interface to review the nightly Job and look for any errors
D. Review the Error Reporting page in the Cloud Console to find any errors.

Antwort: B

Begründung:
Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app //https://cloud.google.com/debugger/docs

251. Frage
Your organization has decided to deploy all its compute workloads to Kubernetes on Google Cloud and two other cloud providers. You want to build an infrastructure-as-code solution to automate the provisioning process for all cloud resources. What should you do?

A. Build the solution by using Terraform, and provision the resources.
B. Build the solution by using Python and the cloud SDKs from all providers to provision the resources.
C. Build the solution by using YAML manifests, and provision the resources.
D. Build the solution by using Config Connector, and provision the resources.

Antwort: A

Begründung:
The requirement is for an infrastructure-as-code (IaC) solution that can manage Kubernetes resources and other cloud resources across multiple cloud providers (Google Cloud and two others).
Option A (YAML manifests): YAML manifests are primarily used for defining Kubernetes objects, not for provisioning general cloud resources (like VPCs, IAM policies, databases) across different cloud providers.
Option C (Python + SDKs): While possible, writing custom scripts using each provider's SDK requires significant development effort to handle state management, dependencies, and provider differences. It essentially reinvents much of what dedicated IaC tools provide and is not a standard IaC approach.
Option D (Config Connector): Config Connector allows managing Google Cloud resources using Kubernetes- style manifests and APIs. It is specific to Google Cloud and cannot manage resources in other cloud providers.
Option B (Terraform): Terraform is an open-source IaC tool explicitly designed for building, changing, and versioning infrastructure safely and efficiently across multiple cloud providers and on-premises data centers.
It uses providers for different platforms (GCP, AWS, Azure, Kubernetes, etc.), allowing a unified workflow to manage diverse resources across the required environments (Google Cloud, other clouds, Kubernetes).
Terraform is the standard tool for multi-cloud IaC automation as described in the scenario.
References:
Terraform on Google Cloud: "Terraform is an open source infrastructure as code (IaC) tool... Terraform lets you manage Google Cloud resources with declarative configuration files..." - https://cloud.google.com/docs
/terraform
Terraform Providers (General): Terraform supports numerous providers for various cloud platforms and services. - https://registry.terraform.io/browse/providers Config Connector Overview: "Config Connector is a Kubernetes addon that allows you to manage Google Cloud resources through Kubernetes." (Google Cloud specific) - https://cloud.google.com/config-connector
/docs/overview

252. Frage
You manage three Google Cloud projects with the Cloud Monitoring API enabled. You want to follow Google-recommended practices to visualize CPU and network metrics for all three projects together. What should you do?

A. 1 Create a Service Account and apply roles/viewer on the three projects
2. Collect metrics and publish them lo the Cloud Monitoring API
3. Add CPU and network Charts for each of the three projects.
B. 1. Create a fourth Google Cloud project
2 Create a Cloud Workspace from the fourth project and add the other three projects
C. 1. Create a Cloud Monitoring Dashboard
2. Collect metrics and publish them into the Pub/Sub topics 3. Add CPU and network Charts (or each of (he three projects
D. 1. Create a Cloud Monitoring Dashboard.
2. Select the CPU and Network metrics from the three projects.
3. Add CPU and network Charts lot each of the three protects.

Antwort: D

253. Frage
You are asked to set up application performance monitoring on Google Cloud projects A, B, and C as a single pane of glass. You want to monitor CPU, memory, and disk. What should you do?

A. Enable API, create a workspace under project A, and then add project B and C.
B. Enable API and then use default dashboards to view all projects in sequence.
C. Enable API and then give the metrics.reader role to projects A, B, and C.
D. Enable API and then share charts from project A, B, and C.

Antwort: B

254. Frage
Your organization uses G Suite for communication and collaboration. All users in your organization have a G Suite account. You want to grant some G Suite users access to your Cloud Platform project. What should you do?

A. Enable Cloud Identity in the GCP Console for your domain.
B. Create a CSV sheet with all users' email addresses. Use the gcloud command line tool to convert them into Google Cloud Platform accounts.
C. Grant them the required IAM roles using their G Suite email address.
D. In the G Suite console, add the users to a special group called cloud-console-users@yourdomain.com.
Rely on the default behavior of the Cloud Platform to grant users access if they are members of this group.

Antwort: C

Begründung:
Reference: https://cloud.google.com/resource-manager/docs/creating-managing-organization Default behavior does not grant access to the "your GCP Project" Default behavior allow only create billing account and project - When the organization is created, all users in your domain are automatically granted Project Creator and Billing Account Creator IAM roles at the organization level. This enables users in your domain to continue creating projects with no disruption.

255. Frage
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