Associate-Cloud-Engineer Exam Dumps - Google Cloud Certified Questions and Answers

Question # 64

You need to immediately change the storage class of an existing Google Cloud bucket. You need to reduce service cost for infrequently accessed files stored in that bucket and for all files that will be added to that bucket in the future. What should you do?

Options:

Use the gsutil to rewrite the storage class for the bucket Change the default storage class for the bucket

Use the gsutil to rewrite the storage class for the bucket Set up Object Lifecycle management on the bucket

Create a new bucket and change the default storage class for the bucket Set up Object Lifecycle management on lite bucket

Create a new bucket and change the default storage class for the bucket import the files from the previous bucket into the new bucket

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Question # 65

You are deploying an application to Cloud Run. Your application requires the use of an API that runs on Google Kubernetes Engine (GKE). You need to ensure that your Cloud Run service can privately reach the API on GKE, and you want to follow Google-recommended practices. What should you do?

Options:

Deploy an ingress resource on the GKE cluster to expose the API to the internet. Use Cloud Armor to filter for IP addresses that can connect to the API. On the Cloud Run service, configure the application to fetch its public IP address and update the Cloud Armor policy on startup to allow this IP address to call the API on ports 80 and 443.

Create an egress firewall rule on the VPC to allow connections to 0.0.0.0/0 on ports 80 and 443.

Create an ingress firewall rule on the VPC to allow connections from 0.0.0.0/0 on ports 80 and 443.

Deploy an internal Application Load Balancer to expose the API on GKE to the VPC. Configure Cloud DNS with the IP address of the internal Application Load Balancer. Deploy a Serverless VPC Access connector to allow the Cloud Run service to call the API through the FQDN on Cloud DNS.

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Answer:

Explanation:

The requirement is for private communication between a Cloud Run service and a GKE API, following best practices.

Option A exposes the GKE API to the public internet, which violates the "privately reach" requirement. Relying on dynamic IP allowlisting with Cloud Armor is complex and less secure than private networking.

Options B and C configure overly permissive firewall rules (allowing all egress or ingress) and do not establish the necessary private network path between Cloud Run (which normally runs outside your VPC) and the GKE cluster within your VPC.

Option D describes the standard Google-recommended pattern for this scenario:

Internal Application Load Balancer (ILB): Expose the GKE service (API) using an ILB. This gives the service a private IP address accessible only within the VPC network (or connected networks).

Cloud DNS: Create a private DNS zone and record pointing a fully qualified domain name (FQDN) to the ILB's private IP address. This allows services to reach the API via a stable name instead of an IP.

Serverless VPC Access Connector: This connector creates a bridge allowing serverless services like Cloud Run to send traffic into your VPC network.

Cloud Run Configuration: Configure the Cloud Run service to use the VPC Access connector. The application code can then call the GKE API using its private FQDN registered in Cloud DNS.

This setup ensures traffic flows entirely over private networks (within the VPC via the ILB and through the VPC Access connector), meeting the private communication requirement securely and reliably.

[References:, Serverless VPC Access: "Serverless VPC Access lets your serverless environment send requests to your VPC network..." - https://cloud.google.com/vpc/docs/serverless-vpc-access, Internal Application Load Balancer Overview: "Google Cloud internal Application Load Balancers are regional, proxy-based Layer 7 load balancers that enable you to run and scale your services behind an internal IP address..." - https://cloud.google.com/load-balancing/docs/internal, Connecting from Cloud Run to a VPC network: Documentation often outlines patterns using VPC Access Connectors and Internal Load Balancers or Private Service Connect. - https://cloud.google.com/run/docs/configuring/connecting-vpc, GKE Internal Load Balancing: How to expose GKE services internally. - https://cloud.google.com/kubernetes-engine/docs/how-to/internal-load-balancing, , , ]

Question # 66

You will have several applications running on different Compute Engine instances in the same project. You want to specify at a more granular level the service account each instance uses when calling Google Cloud APIs. What should you do?

Options:

When creating the instances, specify a Service Account for each instance

When creating the instances, assign the name of each Service Account as instance metadata

After starting the instances, use gcloud compute instances update to specify a Service Account for each instance

After starting the instances, use gcloud compute instances update to assign the name of the relevant Service Account as instance metadata

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Question # 67

(Your digital media company stores a large number of video files on-premises. Each video file ranges from 100 MB to 100 GB. You are currently storing 150 TB of video data in your on-premises network, with no room for expansion. You need to migrate all infrequently accessed video files older than one year to Cloud Storage to ensure that on-premises storage remains available for new files. You must also minimize costs and control bandwidth usage. What should you do?)

Options:

Create a Cloud Storage bucket. Establish an Identity and Access Management (IAM) role with write permissions to the bucket. Use the gsutil tool to directly copy files over the network to Cloud Storage.

Set up a Cloud Interconnect connection between the on-premises network and Google Cloud. Establish a private endpoint for Filestore access. Transfer the data from the existing Network File System (NFS) to Filestore.

Use Transfer Appliance to request an appliance. Load the data locally, and ship the appliance back to Google for ingestion into Cloud Storage.

Use Storage Transfer Service to move the data from the selected on-premises file storage systems to a Cloud Storage bucket.

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Answer:

Explanation:

Let's analyze each option:

A. Using gsutil: While gsutil can transfer data to Cloud Storage, for 150 TB of infrequently accessed data, direct transfer over the network might be slow and consume significant bandwidth, potentially impacting other network operations. It also lacks built-in mechanisms for filtering files based on age.

B. Using Cloud Interconnect and Filestore: Cloud Interconnect provides a dedicated connection, but Filestore is a fully managed NFS service primarily designed for high-performance file sharing for applications running in Google Cloud. Migrating 150 TB of infrequently accessed data to Filestore would be cost-inefficient compared to Cloud Storage and doesn't directly address the requirement of moving older than one year files.

C. Using Transfer Appliance: Transfer Appliance is suitable for very large datasets (petabytes) or when network connectivity is poor or unreliable. While it addresses bandwidth concerns, it involves a physical appliance and might be an overkill for 150 TB of data, especially if network connectivity is reasonable.

D. Using Storage Transfer Service: Storage Transfer Service is specifically designed for moving large amounts of data between online storage systems, including on-premises file systems and Cloud Storage. It offers features like filtering by file age, scheduling transfers, and bandwidth control, directly addressing all the requirements of the question: migrating infrequently accessed files older than one year to Cloud Storage, minimizing costs (by using appropriate Cloud Storage classes for infrequent access), and controlling bandwidth usage.

Google Cloud Documentation References:

Storage Transfer Service Overview: https://cloud.google.com/storage-transfer-service/docs/overview - This page details the capabilities and use cases of Storage Transfer Service, including transferring from on-premises.

Storage Transfer Service for on-premises data: https://cloud.google.com/storage-transfer-service/docs/on-prem-overview - This specifically covers transferring data from on-premises file systems.

Cloud Storage Classes: https://cloud.google.com/storage/docs/storage-classes - Understanding the different storage classes (Standard, Nearline, Coldline, Archive) is crucial for cost optimization of infrequently accessed data. Storage Transfer Service can be configured to move data to a cost-effective class like Nearline or Coldline.

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Question # 68

You are migrating a production-critical on-premises application that requires 96 vCPUs to perform its task. You want to make sure the application runs in a similar environment on GCP. What should you do?

Options:

When creating the VM, use machine type n1-standard-96.

When creating the VM, use Intel Skylake as the CPU platform.

Create the VM using Compute Engine default settings. Use gcloud to modify the running instance to have 96 vCPUs.

Start the VM using Compute Engine default settings, and adjust as you go based on Rightsizing Recommendations.

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Question # 69

You are developing a new web application that will be deployed on Google Cloud Platform. As part of your release cycle, you want to test updates to your application on a small portion of real user traffic. The majority of the users should still be directed towards a stable version of your application. What should you do?

Options:

Deploy me application on App Engine For each update, create a new version of the same service Configure traffic splitting to send a small percentage of traffic to the new version

Deploy the application on App Engine For each update, create a new service Configure traffic splitting to send a small percentage of traffic to the new service.

Deploy the application on Kubernetes Engine For a new release, update the deployment to use the new version

Deploy the application on Kubernetes Engine For a now release, create a new deployment for the new version Update the service e to use the now deployment.

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Question # 70

You are building a backend service for an ecommerce platform that will persist transaction data from mobile and web clients. After the platform is launched, you expect a large volume of global transactions. Your business team wants to run SQL queries to analyze the data. You need to build a highly available and scalable data store for the platform. What should you do?

Options:

Create a multi-region Cloud Spanner instance with an optimized schema.

Create a multi-region Firestore database with aggregation query enabled.

Create a multi-region Cloud SQL for PostgreSQL database with optimized indexes.

Create a multi-region BigQuery dataset with optimized tables.

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Question # 71

You are managing a Data Warehouse on BigQuery. An external auditor will review your company's processes, and multiple external consultants will need view access to the data. You need to provide them with view access while following Google-recommended practices. What should you do?

Options:

Grant each individual external consultant the role of BigQuery Editor

Grant each individual external consultant the role of BigQuery Viewer

Create a Google Group that contains the consultants and grant the group the role of BigQuery Editor

Create a Google Group that contains the consultants, and grant the group the role of BigQuery Viewer

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Question # 72

You are configuring service accounts for an application that spans multiple projects. Virtual machines (VMs) running in the web-applications project need access to BigQuery datasets in the crm-databases project. You want to follow Google-recommended practices to grant access to the service account in the web-applications project. What should you do?

Options:

Grant "project owner" for web-applications appropriate roles to crm-databases.

Grant "project owner" role to crm-databases and the web-applications project.

Grant "project owner" role to crm-databases and roles/bigquery.dataViewer role to web-applications.

Grant roles/bigquery.dataViewer role to crm-databases and appropriate roles to web-applications.

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Question # 73

Your organization is a financial company that needs to store audit log files for 3 years. Your organization has hundreds of Google Cloud projects. You need to implement a cost-effective approach for log file retention. What should you do?

Options:

Create an export to the sink that saves logs from Cloud Audit to BigQuery.

Create an export to the sink that saves logs from Cloud Audit to a Coldline Storage bucket.

Write a custom script that uses logging API to copy the logs from Stackdriver logs to BigQuery.

Export these logs to Cloud Pub/Sub and write a Cloud Dataflow pipeline to store logs to Cloud SQL.

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Exam Code: Associate-Cloud-Engineer

Exam Name: Google Cloud Certified - Associate Cloud Engineer

Last Update: May 21, 2025

Questions: 321

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