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

The Google Cloud Certified - Associate Cloud Engineer content is now fully updated, with all current exam questions added 13 hours ago. Deciding to include Associate-Cloud-Engineer practice exam questions in your study plan goes far beyond basic test preparation.

You'll find that our Associate-Cloud-Engineer exam questions frequently feature detailed scenarios and practical problem-solving exercises that directly mirror industry challenges. Engaging with these Associate-Cloud-Engineer sample sets allows you to effectively manage your time and pace yourself, giving you the ability to finish any Google Cloud Certified - Associate Cloud Engineer practice test comfortably within the allotted time.

Question # 61

You have two Google Cloud projects: project-a with VPC vpc-a (10.0.0.0/16) and project-b with VPC vpc-b (10.8.0.0/16). Your frontend application resides in vpc-a and the backend API services ate deployed in vpc-b. You need to efficiently and cost-effectively enable communication between these Google Cloud projects. You also want to follow Google-recommended practices. What should you do?

Configure a Cloud Router in vpc-a and another Cloud Router in vpc-b.

Configure a Cloud Interconnect connection between vpc-a and vpc-b.

Create VPC Network Peering between vpc-a and vpc-b.

Create an OpenVPN connection between vpc-a and vpc-b.

Question # 62

You created a cluster.YAML file containing

resources:

name: cluster

type: container.v1.cluster

properties:

zone: europe-west1-b

cluster:

description: My GCP ACE cluster

initialNodeCount: 2

You want to use Cloud Deployment Manager to create this cluster in GKE. What should you do?

gcloud deployment-manager deployments create my-gcp-ace-cluster --config cluster.yaml

gcloud deployment-manager deployments create my-gcp-ace-cluster --type container.v1.cluster --config cluster.yaml

gcloud deployment-manager deployments apply my-gcp-ace-cluster --type container.v1.cluster --config cluster.yaml

gcloud deployment-manager deployments apply my-gcp-ace-cluster --config cluster.yaml

Question # 63

You have one GCP account running in your default region and zone and another account running in a non-default region and zone. You want to start a new Compute Engine instance in these two Google Cloud Platform accounts using the command line interface. What should you do?

Create two configurations using gcloud config configurations create [NAME]. Run gcloud config configurations activate [NAME] to switch between accounts when running the commands to start the Compute Engine instances.

Create two configurations using gcloud config configurations create [NAME]. Run gcloud configurations list to start the Compute Engine instances.

Activate two configurations using gcloud configurations activate [NAME]. Run gcloud config list to start the Compute Engine instances.

Activate two configurations using gcloud configurations activate [NAME]. Run gcloud configurations list to start the Compute Engine instances.

Question # 64

(You are developing an internet of things (IoT) application that captures sensor data from multiple devices that have already been set up. You need to identify the global data storage product your company should use to store this data. You must ensure that the storage solution you choose meets your requirements of sub-millisecond latency. What should you do?)

Store the IoT data in Spanner. Use caches to speed up the process and avoid latencies.

Store the IoT data in Bigtable.

Capture IoT data in BigQuery datasets.

Store the IoT data in Cloud Storage. Implement caching by using Cloud CDN.

Answer:

Explanation:

Let ' s evaluate each option based on the requirement of sub-millisecond latency for globally stored IoT data:

A. Spanner with Caching: While Spanner offers strong consistency and global scalability, the base latency might not consistently be sub-millisecond for all read/write operations globally. Introducing caching adds complexity and doesn ' t guarantee sub-millisecond latency for all initial reads or cache misses.

B. Bigtable: Bigtable is a highly scalable NoSQL database service designed for low-latency, high-throughput workloads. It excels at storing and retrieving large volumes of time-series data, which is typical for IoT sensor data. Its architecture is optimized for single-key lookups and scans, providing consistent sub-millisecond latency, making it a strong candidate for this use case.

C. BigQuery: BigQuery is a fully managed, serverless data warehouse designed for analytical queries on large datasets. While it ' s excellent for analyzing IoT data in batch, it ' s not optimized for the low-latency, high-throughput ingestion and retrieval required for real-time IoT applications with sub-millisecond latency needs.

D. Cloud Storage with Cloud CDN: Cloud Storage is object storage and is not designed for low-latency transactional workloads. Cloud CDN is a content delivery network that caches content closer to users for faster delivery, but it ' s not suitable for the primary storage of rapidly incoming IoT sensor data requiring sub-millisecond write latency.

Google Cloud Documentation References:

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This document highlights Bigtable ' s suitability for low-latency and high-throughput applications, including IoT. It mentions its ability to handle massive amounts of data with consistent performance.

Spanner Overview: https://cloud.google.com/spanner/docs/overview - While Spanner offers low latency, Bigtable is generally preferred for extremely high-throughput, low-latency use cases like raw sensor data ingestion due to its optimized architecture for such workloads.

BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - This emphasizes BigQuery ' s analytical capabilities rather than low-latency operational workloads.

Cloud Storage Overview: https://cloud.google.com/storage/docs/overview - This describes Cloud Storage as object storage, not ideal for sub-millisecond latency reads and writes required for real-time IoT data.

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Question # 65

You are planning to migrate your on-premises data to Google Cloud. The data includes:

• 200 TB of video files in SAN storage

• Data warehouse data stored on Amazon Redshift

• 20 GB of PNG files stored on an S3 bucket

You need to load the video files into a Cloud Storage bucket, transfer the data warehouse data into BigQuery, and load the PNG files into a second Cloud Storage bucket. You want to follow Google-recommended practices and avoid writing any code for the migration. What should you do?

Use gcloud storage for the video files. Dataflow for the data warehouse data, and Storage Transfer Service for the PNG files.

Use Transfer Appliance for the videos. BigQuery Data Transfer Service for the data warehouse data, and Storage Transfer Service for the PNG files.

Use Storage Transfer Service for the video files, BigQuery Data Transfer Service for the data warehouse data, and Storage Transfer Service for the PNG files.

Use Cloud Data Fusion for the video files, Dataflow for the data warehouse data, and Storage Transfer Service for the PNG files.

Question # 66

You are building a data lake on Google Cloud for your Internet of Things (loT) application. The loT application has millions of sensors that are constantly streaming structured and unstructured data to your backend in the cloud. You want to build a highly available and resilient architecture based on Google-recommended practices. What should you do?

Stream data to Pub/Sub, and use Dataflow to send data to Cloud Storage

Stream data to Pub/Sub. and use Storage Transfer Service to send data to BigQuery.

Stream data to Dataflow, and use Storage Transfer Service to send data to BigQuery.

Stream data to Dataflow, and use Dataprep by Trifacta to send data to Bigtable.

Question # 67

You want to permanently delete a Pub/Sub topic managed by Config Connector in your Google Cloud project. What should you do?

Use kubect1 to delete the topic resource.

Use gcloud CLI to delete the topic.

Use kubect1 to create the label deleted-by-cnrm and to change its value to true for the topic resource.

Use gcloud CLI to update the topic label managed-by-cnrm to false.

Question # 68

Your customer wants you to create a secure, publicly accessible website with autoscaling based on the compute instance CPU load. You want to enhance performance by storing static content in Cloud Storage. Which resources are needed to distribute the user traffic?

A cross-region internal Application Load Balancer together with Identity-Aware Proxy to allow only HTTPS traffic.

A global external Application Load Balancer with a managed SSL certificate to distribute the load and a URL map to target the requests for the static content to the Cloud Storage backend.

A global external Network Load Balancer pointing to the backend instances to distribute the load evenly. The web servers will forward the request to the Cloud Storage as needed.

A global external Application Load Balancer to distribute the load and a URL map to target the requests for the static content to the Cloud Storage backend. Install the HTTPS certificates on the instance.

Question # 69

You are asked to set up application performance monitoring on Google Cloud projects A, B, and C as a single pane of glass. You want to monitor CPU, memory, and disk. What should you do?

Enable API and then share charts from project A, B, and C.

Enable API and then give the metrics.reader role to projects A, B, and C.

Enable API and then use default dashboards to view all projects in sequence.

Enable API, create a workspace under project A, and then add project B and C.

Question # 70

You have a developer laptop with the Cloud SDK installed on Ubuntu. The Cloud SDK was installed from the Google Cloud Ubuntu package repository. You want to test your application locally on your laptop with Cloud Datastore. What should you do?

Export Cloud Datastore data using gcloud datastore export.

Create a Cloud Datastore index using gcloud datastore indexes create.

Install the google-cloud-sdk-datastore-emulator component using the apt get install command.

Install the cloud-datastore-emulator component using the gcloud components install command.