Associate-Cloud-Engineer Google Cloud Certified - Associate Cloud Engineer exact Exam Questions

Google Cloud Certified - Associate Cloud Engineer

Last Update 13 hours ago Total Questions : 332

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Question # 81

You need to set up a policy so that videos stored in a specific Cloud Storage Regional bucket are moved to Coldline after 90 days, and then deleted after one year from their creation. How should you set up the policy?

Use Cloud Storage Object Lifecycle Management using Age conditions with SetStorageClass and Delete actions. Set the SetStorageClass action to 90 days and the Delete action to 275 days (365 – 90)

Use Cloud Storage Object Lifecycle Management using Age conditions with SetStorageClass and Delete actions. Set the SetStorageClass action to 90 days and the Delete action to 365 days.

Use gsutil rewrite and set the Delete action to 275 days (365-90).

Use gsutil rewrite and set the Delete action to 365 days.

Question # 82

For analysis purposes, you need to send all the logs from all of your Compute Engine instances to a BigQuery dataset called platform-logs. You have already installed the Stackdriver Logging agent on all the instances. You want to minimize cost. What should you do?

1. Give the BigQuery Data Editor role on the platform-logs dataset to the service accounts used by your instances.2. Update your instances’ metadata to add the following value: logs-destination: bq://platform-logs.

1. In Stackdriver Logging, create a logs export with a Cloud Pub/Sub topic called logs as a sink.2. Create a Cloud Function that is triggered by messages in the logs topic.3. Configure that Cloud Function to drop logs that are not from Compute Engine and to insert Compute Engine logs in the platform-logs dataset.

1. In Stackdriver Logging, create a filter to view only Compute Engine logs.2. Click Create Export.3. Choose BigQuery as Sink Service, and the platform-logs dataset as Sink Destination.

1. Create a Cloud Function that has the BigQuery User role on the platform-logs dataset.2. Configure this Cloud Function to create a BigQuery Job that executes this query:INSERT INTO dataset.platform-logs (timestamp, log)SELECT timestamp, log FROM compute.logsWHERE timestamp > DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY)3. Use Cloud Scheduler to trigger this Cloud Function once a day.

Question # 83

You are hosting an application on bare-metal servers in your own data center. The application needs access to Cloud Storage. However, security policies prevent the servers hosting the application from having public IP addresses or access to the internet. You want to follow Google-recommended practices to provide the application with access to Cloud Storage. What should you do?

1. Use nslookup to get the IP address for storage.googleapis.com.2. Negotiate with the security team to be able to give a public IP address to the servers.3. Only allow egress traffic from those servers to the IP addresses for storage.googleapis.com.

1. Using Cloud VPN, create a VPN tunnel to a Virtual Private Cloud (VPC) in Google Cloud Platform (GCP).2. In this VPC, create a Compute Engine instance and install the Squid proxy server on this instance.3. Configure your servers to use that instance as a proxy to access Cloud Storage.

1. Use Migrate for Compute Engine (formerly known as Velostrata) to migrate those servers to Compute Engine.2. Create an internal load balancer (ILB) that uses storage.googleapis.com as backend.3. Configure your new instances to use this ILB as proxy.

1. Using Cloud VPN or Interconnect, create a tunnel to a VPC in GCP.2. Use Cloud Router to create a custom route advertisement for 199.36.153.4/30. Announce that network to your on-premises network through the VPN tunnel.3. In your on-premises network, configure your DNS server to resolve *.googleapis.com as a CNAME to restricted.googleapis.com.

Answer:

Explanation:

Our requirement is to follow Google recommended practices to achieve the end result. Configuring Private Google Access for On-Premises Hosts is best achieved by VPN/Interconnect + Advertise Routes + Use restricted Google IP Range.

Using Cloud VPN or Interconnect, create a tunnel to a VPC in GCP

Using Cloud Router to create a custom route advertisement for 199.36.153.4/30. Announce that network to your on-premises network through the VPN tunnel.

In your on-premises network, configure your DNS server to resolve *.googleapis.com as a CNAME to restricted.googleapis.com is the right answer right, and it is what Google recommends.

Ref: https://cloud.google.com/vpc/docs/configure-private-google-access-hybrid

You must configure routes so that Google API traffic is forwarded through your Cloud VPN or Cloud Interconnect connection, firewall rules on your on-premises firewall to allow the outgoing traffic, and DNS so that traffic to Google APIs resolves to the IP range youve added to your routes.

You can use Cloud Router Custom Route Advertisement to announce the Restricted Google APIs IP addresses through Cloud Router to your on-premises network. The Restricted Google APIs IP range is 199.36.153.4/30. While this is technically a public IP range, Google does not announce it publicly. This IP range is only accessible to hosts that can reach your Google Cloud projects through internal IP ranges, such as through a Cloud VPN or Cloud Interconnect connection. Without having a public IP address or access to the internet, the only way you could connect to cloud storage is if you have an internal route to it.

So Negotiate with the security team to be able to give public IP addresses to the servers is not right. Following Google recommended practices is synonymous with using Googles services (Not quite, but it is at least for the exam !!).

So In this VPC, create a Compute Engine instance and install the Squid proxy server on this instance is not right.

Migrating the VM to Compute Engine is a bit drastic when Google says it is perfectly fine to have Hybrid Connectivity architectures https://cloud.google.com/hybrid-connectivity.

So,

Use Migrate for Compute Engine (formerly known as Velostrata) to migrate these servers to Compute Engine is not right.

Question # 84

You have 32 GB of data in a single file that you need to upload to a Nearline Storage bucket. The WAN connection you are using is rated at 1 Gbps, and you are the only one on the connection. You want to use as much of the rated 1 Gbps as possible to transfer the file rapidly. How should you upload the file?

Use the GCP Console to transfer the file instead of gsutil.

Enable parallel composite uploads using gsutil on the file transfer.

Decrease the TCP window size on the machine initiating the transfer.

Change the storage class of the bucket from Nearline to Multi-Regional.

Question # 85

Your company has an internal application for managing transactional orders. The application is used exclusively by employees in a single physical location. The application requires strong consistency, fast queries, and ACID guarantees for multi-table transactional updates. The first version of the application is implemented inPostgreSQL, and you want to deploy it to the cloud with minimal code changes. Which database is most appropriate for this application?

BigQuery

Cloud SQL

Cloud Spanner

Cloud Datastore

Question # 86

You are deploying a production application on Compute Engine. You want to prevent anyone from accidentally destroying the instance by clicking the wrong button. What should you do?

Disable the flag “Delete boot disk when instance is deleted.”

Enable delete protection on the instance.

Disable Automatic restart on the instance.

Enable Preemptibility on the instance.

Question # 87

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?

The pending Pod ' s 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.

The node pool is configured with a service account that does not have permission to pull the container image used by the pending Pod.

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.

Question # 88

Your company developed an application to deploy on Google Kubernetes Engine. Certain parts of the application are not fault-tolerant and are allowed to have downtime Other parts of the application are critical and must always be available. You need to configure a Goorj e Kubernfl:es Engine duster while optimizing for cost. What should you do?

Create a cluster with a single node-pool by using standard VMs. Label the fault-tolerant Deployments as spot-true.

Create a cluster with a single node-pool by using Spot VMs. Label the critical Deployments as spot-false.

Create a cluster with both a Spot W node pool and a rode pool by using standard VMs Deploy the critical.deployments on the Spot VM node pool and the fault; tolerant deployments on the node pool by using standard VMs.

Create a cluster with both a Spot VM node pool and by using standard VMs. Deploy the critical deployments on the mode pool by using standard VMs and the fault-tolerant deployments on the Spot VM node pool.

Question # 89

You create a new Google Kubernetes Engine (GKE) cluster and want to make sure that it always runs a supported and stable version of Kubernetes. What should you do?

Enable the Node Auto-Repair feature for your GKE cluster.

Enable the Node Auto-Upgrades feature for your GKE cluster.

Select the latest available cluster version for your GKE cluster.

Select “Container-Optimized OS (cos)” as a node image for your GKE cluster.

Question # 90

You have developed an application that consists of multiple microservices, with each microservice packaged in its own Docker container image. You want to deploy the entire application on Google Kubernetes Engine so that each microservice can be scaled individually. What should you do?

Create and deploy a Custom Resource Definition per microservice.

Create and deploy a Docker Compose File.

Create and deploy a Job per microservice.

Create and deploy a Deployment per microservice.