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Free Google Associate Cloud Engineer Exam Braindumps (page: 22)

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PDF with 290 Questions

You are operating a Google Kubernetes Engine (GKE) cluster for your company where different teams can run non-production workloads. Your Machine Learning (ML) team needs access to Nvidia Tesla P100 GPUs to train their models. You want to minimize effort and cost.
What should you do?

  1. Ask your ML team to add the "accelerator: gpu" annotation to their pod specification.
  2. Recreate all the nodes of the GKE cluster to enable GPUs on all of them.
  3. Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs.
    Dedicate this cluster to your ML team.
  4. Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.

Answer(s): D

Explanation:

This is the most optimal solution. Rather than recreating all nodes, you create a new node pool with GPU enabled. You then modify the pod specification to target particular GPU types by adding node selector to your workloads Pod specification. YOu still have a single cluster so you pay Kubernetes cluster management fee for just one cluster thus minimizing the cost. Ref: https://cloud.google.com/kubernetes-engine/docs/how-to/gpus Ref: https://cloud.google.com/kubernetes-engine/pricing

Example:
apiVersion: v1
kind: Pod metadata:
name: my-gpu-pod spec:
containers:
name: my-gpu-container image: nvidia/cuda:10.0-runtime-ubuntu18.04
command: [/bin/bash]
resources:
limits:
nvidia.com/gpu: 2
nodeSelector:
cloud.google.com/gke-accelerator: nvidia-tesla-k80 # or nvidia-tesla-p100 or nvidia-tesla-p4 or nvidia-tesla-v100 or nvidia-tesla-t4



Your VMs are running in a subnet that has a subnet mask of 255.255.255.240. The current subnet has no more free IP addresses and you require an additional 10 IP addresses for new VMs. The existing and new VMs should all be able to reach each other without additional routes.
What should you do?

  1. Use gcloud to expand the IP range of the current subnet.
  2. Delete the subnet, and recreate it using a wider range of IP addresses.
  3. Create a new project. Use Shared VPC to share the current network with the new project.
  4. Create a new subnet with the same starting IP but a wider range to overwrite the current subnet.

Answer(s): A

Explanation:

https://cloud.google.com/sdk/gcloud/reference/compute/networks/subnets/expand-ip-range gcloud compute networks subnets expand-ip-range - expand the IP range of a Compute Engine subnetwork gcloud compute networks subnets expand-ip-range NAME --prefix- length=PREFIX_LENGTH [--region=REGION] [GCLOUD_WIDE_FLAG ...]



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?

  1. Enable Cloud Identity in the GCP Console for your domain.
  2. Grant them the required IAM roles using their G Suite email address.
  3. Create a CSV sheet with all users' email addresses. Use the gcloud command line tool to convert them into Google Cloud Platform accounts.
  4. 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.

Answer(s): B


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.



You have a Google Cloud Platform account with access to both production and development projects. You need to create an automated process to list all compute instances in development and production projects on a daily basis.
What should you do?

  1. Create two configurations using gcloud config. Write a script that sets configurations as active, individually. For each configuration, use gcloud compute instances list to get a list of compute resources.
  2. Create two configurations using gsutil config. Write a script that sets configurations as active, individually. For each configuration, use gsutil compute instances list to get a list of compute resources.
  3. Go to Cloud Shell and export this information to Cloud Storage on a daily basis.
  4. Go to GCP Console and export this information to Cloud SQL on a daily basis.

Answer(s): A

Explanation:

You can create two configurations ­ one for the development project and another for the production project. And you do that by running "gcloud config configurations create" command. https://cloud.google.com/sdk/gcloud/reference/config/configurations/create In your custom script, you can load these configurations one at a time and execute gcloud compute instances list to list Google Compute Engine instances in the project that is active in the gcloud configuration.
Ref: https://cloud.google.com/sdk/gcloud/reference/compute/instances/list Once you have this information, you can export it in a suitable format to a suitable target e.g. export as CSV or export to Cloud Storage/BigQuery/SQL, etc






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