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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Setting up a cloud solution environment	20%	- Managing billing and identity access - Setting up cloud projects and accounts
Configuring access and security	15%	- Managing IAM roles and permissions - Securing data and services
Planning and configuring a cloud solution	25%	- Configuring compute resources - Configuring storage and database solutions
Deploying and implementing a cloud solution	20%	- Implementing networking components - Deploying applications and services
Ensuring successful operation of a cloud solution	20%	- Managing cloud resources - Monitoring and logging

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Google Associate Cloud Engineer Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
Your team is running an on-premises ecommerce application. The application contains a complex set of microservices written in Python, and each microservice is running on Docker containers. Configurations are injected by using environment variables. You need to deploy your current application to a serverless Google Cloud cloud solution. What should you do?

A. Use your existing codebase and deploy each service as a separate Cloud Run Use the same configurations as on-premises.
B. Use your existing continuous integration and delivery (CI/CD) pipeline. Use the generated Docker images and deploy them to Cloud Function. Use the same configuration as on-premises.
C. Use your existing CI/CD pipeline Use the generated Docker images and deploy them to Cloud Run.
Update the configurations and the required endpoints.
D. Use the existing codebase and deploy each service as a separate Cloud Function Update the configurations and the required endpoints.

Answer: C

NEW QUESTION # 51
Your management has asked an external auditor to review all the resources in a specific project. The security team has enabled the Organization Policy called Domain Restricted Sharing on the organization node by specifying only your Cloud Identity domain. You want the auditor to only be able to view, but not modify, the resources in that project. What should you do?

A. Create a temporary account for the auditor in Cloud Identity, and give that account the Security Reviewer role on the project.
B. Create a temporary account for the auditor in Cloud Identity, and give that account the Viewer role on the project.
C. Ask the auditor for their Google account, and give them the Security Reviewer role on the project.
D. Ask the auditor for their Google account, and give them the Viewer role on the project.

Answer: B

Explanation:
Explanation
Using primitive roles The following table lists the primitive roles that you can grant to access a project, the description of what the role does, and the permissions bundled within that role. Avoid using primitive roles except when absolutely necessary. These roles are very powerful, and include a large number of permissions across all Google Cloud services. For more details on when you should use primitive roles, see the Identity and Access Management FAQ. IAM predefined roles are much more granular, and allow you to carefully manage the set of permissions that your users have access to. See Understanding Roles for a list of roles that can be granted at the project level. Creating custom roles can further increase the control you have over user permissions. https://cloud.google.com/resource-manager/docs/access-control-proj#using_primitive_roles
https://cloud.google.com/iam/docs/understanding-custom-roles

NEW QUESTION # 52
You used the gcloud container clusters command to create two Google Cloud Kubernetes (GKE) clusters prod-cluster and dev-cluster.
* prod-cluster is a standard cluster.
* dev-cluster is an auto-pilot duster.
When you run the Kubect1 get nodes command, you only see the nodes from prod-cluster Which commands should you run to check the node status for dev-cluster?

Answer: D

NEW QUESTION # 53
You need to create a copy of a custom Compute Engine virtual machine (VM) to facilitate an expected increase in application traffic due to a business acquisition. What should you do?

A. Create a Compute Engine snapshot of your base VM. Create your images from that snapshot.
B. Create a custom Compute Engine image from a snapshot. Create your images from that image.
C. Create a Compute Engine snapshot of your base VM. Create your instances from that snapshot.
D. Create a custom Compute Engine image from a snapshot. Create your instances from that image.

Answer: D

Explanation:
A custom image belongs only to your project. To create an instance with a custom image, you must first have a custom image.
Reference:
Preparing your instance for an image
You can create an image from a disk even while it is attached to a running VM instance. However, your image will be more reliable if you put the instance in a state that is easier for the image to capture. Use one of the following processes to prepare your boot disk for the image:
Stop the instance so that it can shut down and stop writing any data to the persistent disk.
If you can't stop your instance before you create the image, minimize the amount of writes to the disk and sync your file system.
Pause apps or operating system processes that write data to that persistent disk.
Run an app flush to disk if necessary. For example, MySQL has a FLUSH statement. Other apps might have similar processes.
Stop your apps from writing to your persistent disk.
Run sudo sync.
After you prepare the instance, create the image.
https://cloud.google.com/compute/docs/images/create-delete-deprecate-private-images#prepare_instance_for_image

NEW QUESTION # 54
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 Cloud Run and GPU as a platform to run the fine-tuning jobs.
B. Use Dataflow as a platform to run the fine-tuning jobs.
C. Use Google Kubernetes Engine (GKE) and hardware accelerators as a platform to run the fine-tuning jobs.
D. Use a Compute Engine managed instance group as a platform to deploy Jupyter Notebooks and run fine- tuning jobs.

Answer: C

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 # 55
......

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