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Google Professional-Cloud-Architect Sample Questions – Free Practice Test & Real Exam Prep

Question #1

For this question, refer to the Helicopter Racing League (HRL) case study. Recently HRLstarted a new regionalracing league in Cape Town, South Africa. In an effort to give customers in Cape Town abetter userexperience, HRL has partnered with the Content Delivery Network provider, Fastly. HRLneeds to allow trafficcoming from all of the Fastly IP address ranges into their Virtual Private Cloud network(VPC network). You area member of the HRL security team and you need to configure the update that will allowonly the Fastly IPaddress ranges through the External HTTP(S) load balancer. Which command should youuse?

A. glouc compute firewall rules update hlr-policy \--priority 1000 \target tags-sourceiplist fastly \--allow tcp:443
B. gcloud compute security policies rules update 1000 \--security-policy hlr-policy \--expression "evaluatePreconfiguredExpr('sourceiplist-fastly')" \--action " allow"
C. gcloud compute firewall rules updatesourceiplist-fastly \priority 1000 \allow tcp: 443
D. gcloud compute priority-policies rules update1000 \security policy from fastly--src- ip-ranges"-- action " allow"

Question #2

For this question, refer to the Helicopter Racing League (HRL) case study. HRL wantsbetter predictionaccuracy from their ML prediction models. They want you to use Google’s AI Platform soHRL can understandand interpret the predictions. What should you do?

A. Use Explainable AI.
B. Use Vision AI.
C. Use Google Cloud’s operations suite.
D. Use Jupyter Notebooks.

Question #3

For this question, refer to the Helicopter Racing League (HRL) case study. HRL is lookingfor a cost-effectiveapproach for storing their race data such as telemetry. They want to keep all historicalrecords, train modelsusing only the previous season's data, and plan for data growth in terms of volume andinformation collected.You need to propose a data solution. Considering HRL business requirements and thegoals expressed byCEO S. Hawke, what should you do?

A. Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race databy season and event.
B. Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race datausing season as a primary key.
C. Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
D. Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Useseparate database instances for each season.

Question #4

For this question, refer to the Helicopter Racing League (HRL) case study. The HRLdevelopment teamreleases a new version of their predictive capability application every Tuesday evening at 3a.m. UTC to arepository. The security team at HRL has developed an in-house penetration test CloudFunction called Airwolf.The security team wants to run Airwolf against the predictive capability application as soonas it is releasedevery Tuesday. You need to set up Airwolf to run at the recurring weekly cadence. Whatshould you do?

A. Set up Cloud Tasks and a Cloud Storage bucket that triggers a Cloud Function.
B. Set up a Cloud Logging sink and a Cloud Storage bucket that triggers a Cloud Function.
C. Configure the deployment job to notify a Pub/Sub queue that triggers a Cloud Function.
D. Set up Identity and Access Management (IAM) and Confidential Computing to trigger a Cloud Function.

Question #5

You are monitoring Google Kubernetes Engine (GKE) clusters in a Cloud Monitoringworkspace. As a Site Reliability Engineer (SRE), you need to triage incidents quickly. Whatshould you do?

A. Navigate the predefined dashboards in the Cloud Monitoring workspace, and then addmetrics and create alert policies.
B. Navigate the predefined dashboards in the Cloud Monitoring workspace, create custommetrics, and install alerting software on a Compute Engine instance.
C. Write a shell script that gathers metrics from GKE nodes, publish these metrics to aPub/Sub topic, export the data to BigQuery, and make a Data Studio dashboard.
D. Create a custom dashboard in the Cloud Monitoring workspace for each incident, andthen add metrics and create alert policies.

Question #6

You are designing a Data Warehouse on Google Cloud and want to store sensitive data inBigQuery. Your company requires you to generate encryption keys outside of GoogleCloud. You need to implement a solution. What should you do?

A. Generate a new key in Cloud Key Management Service (Cloud KMS). Store all data inCloud Storage using the customer-managed key option and select the created key. Set upa Dataflow pipeline to decrypt the data and to store it in a BigQuery dataset.
B. Generate a new key in Cloud Key Management Service (Cloud KMS). Create a datasetin BigQuery using the customer-managed key option and select the created key
C. Import a key in Cloud KMS. Store all data in Cloud Storage using the customermanagedkey option and select the created key. Set up a Dataflow pipeline to decrypt thedata and to store it in a new BigQuery dataset.
D. Import a key in Cloud KMS. Create a dataset in BigQuery using the customer-suppliedkey option and select the created key.

Question #7

Your team is developing a web application that will be deployed on Google KubernetesEngine (GKE). Your CTO expects a successful launch and you need to ensure yourapplication can handle the expected load of tens of thousands of users. You want to testthe current deployment to ensure the latency of your application stays below a certainthreshold. What should you do?

A. Use a load testing tool to simulate the expected number of concurrent users and totalrequests to your application, and inspect the results.
B. Enable autoscaling on the GKE cluster and enable horizontal pod autoscaling on yourapplication deployments. Send curl requests to your application, and validate if the autoscaling works.
C. Replicate the application over multiple GKE clusters in every Google Cloud region.Configure a global HTTP(S) load balancer to expose the different clusters over a single global IP address.
D. Use Cloud Debugger in the development environment to understand the latencybetween the different microservices.

Question #8

An application development team has come to you for advice.They are planning to write and deploy an HTTP(S) API using Go 1.12. The API will have a very unpredictableworkload and must remain reliable during peaks in traffic. They want to minimizeoperational overhead for this application. What approach should you recommend?

A. Use a Managed Instance Group when deploying to Compute Engine
B. Develop an application with containers, and deploy to Google Kubernetes Engine (GKE)
C. Develop the application for App Engine standard environment
D. Develop the application for App Engine Flexible environment using a custom runtime

Question #9

Your company has a Google Cloud project that uses BlgQuery for data warehousing Thereare some tables that contain personally identifiable information (PI!) Only the complianceteam may access the PH. The other information in the tables must be available to the datascience team. You want to minimize cost and the time it takes to assign appropriate accessto the tables What should you do?

A. 1 From the dataset where you have the source data, create views of tables that youwant to share, excluding Pll2 Assign an appropriate project-level IAM role to the members of the data science team3 Assign access controls to the dataset that contains the view
B. 1 From the dataset where you have the source data, create materialized views of tablesthat you want to share excluding Pll2 Assign an appropriate project-level IAM role to the members of the data science team 3.Assign access controls to the dataset that contains the view.
C. 1 Create a dataset for the data science team2 Create views of tables that you want to share excluding Pll3 Assign an appropriate project-level IAM role to the members of the data science team4 Assign access controls to the dataset that contains the view5 Authorize the view to access the source dataset
D. 1. Create a dataset for the data science team.2. Create materialized views of tables that you want to share, excluding Pll3. Assign an appropriate project-level IAM role to the members of the data science team4 Assign access controls to the dataset that contains the view5 Authorize the view to access the source dataset

Question #10

You want to allow your operations learn to store togs from all the production protects inyour Organization, without during logs from other projects All of the production projects arecontained in a folder. You want to ensure that all logs for existing and new productionprojects are captured automatically. What should you do?

A. Create an aggregated export on the Production folder. Set the log sink to be a CloudStorage bucket in an operations project
B. Create an aggregated export on the Organization resource. Set the tog sink to be aCloud Storage bucket in an operations project.
C. Create log exports in the production projects. Set the log sinks to be a Cloud Storage bucket in an operations project.
D. Create tog exports in the production projects. Set the tog sinks to be BigQuery datasetsin the production projects and grant IAM access to the operations team to run queries onthe datasets

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