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Google Certified Professional - Cloud Architect (GCP)

Last Update 12 hours ago Total Questions : 345

The Google Certified Professional - Cloud Architect (GCP) content is now fully updated, with all current exam questions added 12 hours ago. Deciding to include Professional-Cloud-Architect practice exam questions in your study plan goes far beyond basic test preparation.

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

Question # 51

The current Dress4win system architecture has high latency to some customers because it is located in one

data center.

As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute it ' s system

architecture to multiple locations when Google cloud platform.

Which approach should they use?

A.

Use regional managed instance groups and a global load balancer to increase performance because the

regional managed instance group can grow instances in each region separately based on traffic.

B.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines managed by your operations team.

C.

Use regional managed instance groups and a global load balancer to increase reliability by providing

automatic failover between zones in different regions.

D.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines as part of a separate managed instance groups.

Question # 52

For this question, refer to the Dress4Win case study.

Dress4Win has asked you to recommend machine types they should deploy their application servers to. How should you proceed?

A.

Perform a mapping of the on-premises physical hardware cores and RAM to the nearest machine types in the cloud.

B.

Recommend that Dress4Win deploy application servers to machine types that offer the highest RAM to CPU ratio available.

C.

Recommend that Dress4Win deploy into production with the smallest instances available, monitor them over time, and scale the machine type up until the desired performance is reached.

D.

Identify the number of virtual cores and RAM associated with the application server virtual machines align them to a custom machine type in the cloud, monitor performance, and scale the machine types up until the desired performance is reached.

Question # 53

For this question, refer to the Dress4Win case study.

As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?

A.

Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer ' s ID and their image files.

B.

Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer ' s unique ID.

C.

Use a distributed file system to store customers ' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file ' s owner attribute, ensuring privacy of images.

D.

Use a distributed file system to store customers ' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer ' s ID to their image files.

Question # 54

For this question, refer to the Dress4Win case study.

As part of Dress4Win ' s plans to migrate to the cloud, they want to be able to set up a managed logging and monitoring system so they can handle spikes in their traffic load. They want to ensure that:

• The infrastructure can be notified when it needs to scale up and down to handle the ebb and flow of usage throughout the day

• Their administrators are notified automatically when their application reports errors.

• They can filter their aggregated logs down in order to debug one piece of the application across many hosts

Which Google StackDriver features should they use?

A.

Logging, Alerts, Insights, Debug

B.

Monitoring, Trace, Debug, Logging

C.

Monitoring, Logging, Alerts, Error Reporting

D.

Monitoring, Logging, Debug, Error Report

Question # 55

For this question, refer to the Dress4Win case study.

You want to ensure Dress4Win ' s sales and tax records remain available for infrequent viewing by auditors for at least 10 years. Cost optimization is your top priority. Which cloud services should you choose?

A.

Google Cloud Storage Coldline to store the data, and gsutil to access the data.

B.

Google Cloud Storage Nearline to store the data, and gsutil to access the data.

C.

Google Bigtabte with US or EU as location to store the data, and gcloud to access the data.

D.

BigQuery to store the data, and a web server cluster in a managed instance group to access the data. Google Cloud SQL mirrored across two distinct regions to store the data, and a Redis cluster in a managed instance group to access the data.

Question # 56

For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

A.

Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.

B.

Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.

C.

Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage

bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.

D.

Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use Pig scripts to analyze data.

Question # 57

TerramEarth has about 1 petabyte (PB) of vehicle testing data in a private data center. You want to move the data to Cloud Storage for your machine learning team. Currently, a 1-Gbps interconnect link is available for you. The machine learning team wants to start using the data in a month. What should you do?

A.

Request Transfer Appliances from Google Cloud, export the data to appliances, and return the appliances to Google Cloud.

B.

Configure the Storage Transfer service from Google Cloud to send the data from your data center to Cloud Storage

C.

Make sure there are no other users consuming the 1 Gbps link, and use multi-thread transfer to upload the data to Cloud Storage.

D.

Export files to an encrypted USB device, send the device to Google Cloud, and request an import of the data to Cloud Storage

Question # 58

For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to

BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an

automated daily basis while managing cost.

What should you do?

A.

Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.

B.

Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.

C.

Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.

D.

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

Question # 59

For this question, refer to the TerramEarth case study.

You start to build a new application that uses a few Cloud Functions for the backend. One use case requires a Cloud Function func_display to invoke another Cloud Function func_query. You want func_query only to accept invocations from func_display. You also want to follow Google ' s recommended best practices. What should you do?

A.

Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request Pass the same token to func _query and reject the invocation if the tokens are different.

B.

Make func_query ' Require authentication. ' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

C.

Make func _query ' Require authentication ' and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.

D.

Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.

Question # 60

You are migrating a Linux-based application from your private data center to Google Cloud. The TerramEarth security team sent you several recent Linux vulnerabilities published by Common Vulnerabilities and Exposures (CVE). You need assistance in understanding how these vulnerabilities could impact your migration. What should you do?

A.

Open a support case regarding the CVE and chat with the support engineer.

B.

Read the CVEs from the Google Cloud Status Dashboard to understand the impact.

C.

Read the CVEs from the Google Cloud Platform Security Bulletins to understand the impact

D.

Post a question regarding the CVE in Stack Overflow to get an explanation

E.

Post a question regarding the CVE in a Google Cloud discussion group to get an explanation

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