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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Configuring Access and Security	15-20%	- Managing service accounts 1. Creating and managing service accounts 2. Assigning service accounts to resources 3. Using service account keys - Managing IAM resources 1. Defining custom IAM roles 2. Creating and managing IAM roles 3. Applying IAM policies to resources - Viewing audit logs 1. Configuring audit log policies 2. Accessing and analyzing Cloud Audit Logs
Ensuring Successful Operation	15-20%	- Managing storage and data solutions 1. Managing Cloud Storage buckets and objects 2. Configuring CORS and encryption 3. Setting lifecycle policies - Managing Compute Engine resources 1. Managing instances, disks, and snapshots 2. Attaching and detaching disks 3. Resizing instances and persistent disks - 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. Managing workloads (Deployments, Services, HPA) 3. Upgrading clusters - 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%	- 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 - Managing billing configuration 1. Linking billing to projects 2. Creating billing accounts 3. Setting up budget alerts and notifications
Deploying and Implementing a Cloud Solution	20-25%	- 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 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 applications with Cloud Deployment Manager 1. Using templates for resource provisioning 2. Creating deployment configurations - 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
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 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. Configuring persistent disks and local SSDs 2. Selecting appropriate machine types and zones

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Google Associate Cloud Engineer Exam Sample Questions (Q351-Q356):

NEW QUESTION # 351
Your company ' s machine learning team requires a scalable and flexible platform to fine-tune large language models utilizing a large volume of proprietary data on Google Cloud. You are tasked with building a solution for this team. What should you do?

A. Use Google Kubernetes Engine (GKE) and hardware accelerators as a platform to run the fine-tuning jobs.
B. Use Cloud Run and GPU as a platform to run the fine-tuning jobs.
C. Use a Compute Engine managed instance group as a platform to deploy Jupyter Notebooks and run fine- tuning jobs.
D. Use Dataflow as a platform to run the fine-tuning jobs.

Answer: A

Explanation:
The correct answer is C because fine-tuning large language models requires scalable compute orchestration and hardware acceleration. GKE is appropriate for containerized ML workloads that need flexible scheduling, repeatability, and access to accelerator-backed nodes. Google Cloud documentation specifically supports running compute-intensive workloads such as AI workloads on GKE Standard node pools with NVIDIA GPU hardware accelerators. Dataflow is primarily for data processing pipelines, not model fine-tuning. A managed instance group with Jupyter Notebooks is less flexible and less Kubernetes-native for distributed ML execution. Cloud Run can be useful for serverless services and some GPU workloads, but large-scale training and fine-tuning are better matched to GKE with accelerators.

NEW QUESTION # 352
Youare configuring Cloud DNS. You want !to create DNS records to
pointhome.mydomain.com,mydomain.com. andwww.mydomain.comto the IP address of your Google Cloud load balancer. What should you do?

A. Create one CNAME record to point mydomain.com to the load balancer, and create two A records to point WWW and HOME lo mydomain.com respectively.
B. Create one CNAME record to point mydomain.com to the load balancer, and create two AAAA records to point WWW and HOME to mydomain.com respectively.
C. Create one A record to point mydomain.com to the load balancer, and create two CNAME records to point WWW and HOME to mydomain.com respectively.
D. Create one A record to point mydomain.com lo the load balancer, and create two NS records to point WWW and HOME to mydomain.com respectively.

Answer: C

NEW QUESTION # 353
You are managing your company's archival records that are stored in a Cloud Storage bucket using the Multi- Regional storage class. These objects are rarely accessed after 90 days but must be retained indefinitely to meet regulatory compliance requirements. You need to implement a cost-effective process that automatically transitions these objects to the lowest-cost storage class suitable for long-term retention immediately after the
90-day period. What should you do?

A. Create an Object Lifecycle Management rule on the bucket that specifies the createdBefore condition set to 90 days ago and uses the Delete action to remove the objects.
B. Enable Object Versioning on the bucket, and create an Object Lifecycle Management rule that sets the daysSinceNoncurrentTime condition to 90 days, transitioning noncurrent versions to Archive storage.
C. Create a nightly scheduled Cloud Run function that uses the Cloud Storage API to change the storage class of all objects older than 90 days to Archive storage.
D. Create an Object Lifecycle Management rule on the bucket that specifies an age condition of 90 days and uses the SetStorageClass action to transition the objects to Archive storage.

Answer: D

Explanation:
The correct answer is B because Cloud Storage Object Lifecycle Management is designed to automate storage-class transitions based on object conditions such as object age. The requirement is not to delete the records, but to retain them indefinitely while reducing cost after 90 days. Archive storage is the lowest-cost class for long-term retained data that is rarely accessed. The SetStorageClass lifecycle action changes the storage class of an object when the rule conditions are met. Object Versioning and daysSinceNoncurrentTime apply to noncurrent object versions, which is not the scenario. A scheduled Cloud Run function would add unnecessary operational overhead. A Delete action would violate the retention requirement.

NEW QUESTION # 354
You have an application running in Google Kubernetes Engine (GKE) with cluster autoscaling enabled. The application exposes a TCP endpoint. There are several replicas of this application. You have a Compute Engine instance in the same region, but in another Virtual Private Cloud (VPC), called gce-network, that has no overlapping IP ranges with the first VPC. This instance needs to connect to the application on GKE. You want to minimize effort. What should you do?

A. 1. In GKE, create a Serviceof type LoadBalancer that uses the application's Pods as backend.
2. Set the service's externalTrafficPolicyto Cluster.
3. Configure the Compute Engine instance to use the address of the load balancer that has been created.
B. 1. In GKE, create a Serviceof type LoadBalancerthat uses the application's Pods as backend.
2. Add an annotation to this service: cloud.google.com/load-balancer-type: Internal
3. Peer the two VPCs together.
4. Configure the Compute Engine instance to use the address of the load balancer that has been created.
C. 1. In GKE, create a Serviceof type LoadBalancerthat uses the application's Pods as backend.
2. Add a Cloud Armor Security Policy to the load balancer that whitelists the internal IPs of the MIG's instances.
3. Configure the Compute Engine instance to use the address of the load balancer that has been created.
D. 1. In GKE, create a Service of type NodePortthat uses the application's Pods as backend.
2. Create a Compute Engine instance called proxy with 2 network interfaces, one in each VPC.
3. Use iptables on this instance to forward traffic from gce-network to the GKE nodes.
4. Configure the Compute Engine instance to use the address of proxyin gce-networkas endpoint.

Answer: A

NEW QUESTION # 355
You have one project called proj-sa where you manage all your service accounts. You want to be able to use a service account from this project to take snapshots of VMs running in another project called proj-vm. What should you do?

A. Download the private key from the service account, and add the private key to each VM's SSH keys.
B. When creating the VMs, set the service account's API scope for Compute Engine to read/write.
C. Download the private key from the service account, and add it to each VMs custom metadata.
D. Grant the service account the IAM Role of Compute Storage Admin in the project called proj-vm.

Answer: D

NEW QUESTION # 356
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