[Q160-Q177] Exam Passing Guarantee Oct 01, 2026 Professional-Cloud-Architect Exam with Accurate Quastions!

Posted On October 1, 2026

Exam Passing Guarantee Oct 01, 2026 Professional-Cloud-Architect Exam with Accurate Quastions!

Test Engine to Practice Test for Professional-Cloud-Architect Valid and Updated Dumps

The Google Professional-Cloud-Architect exam covers a wide range of topics, including cloud solution design, GCP infrastructure, data storage and processing, security, compliance, and cost optimization. Candidates are expected to have a deep understanding of GCP services, architecture principles, and best practices for cloud computing. Professional-Cloud-Architect exam also tests soft skills such as communication, project management, and problem-solving.

Google Professional-Cloud-Architect certification is highly regarded in the IT industry and is recognized as a benchmark for cloud architects. It is a valuable asset for professionals who want to advance their career in cloud architecture and demonstrate their expertise to potential employers. Google Certified Professional – Cloud Architect (GCP) certification demonstrates a candidate’s ability to design and manage solutions on GCP, as well as their understanding of cloud architecture best practices.

NO.160 An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data.
You want to help them find a solution that meets their needs.
What should you do?

Direct them to download and install the Google StackDriver logging agent

Send them a list of online resources about logging best practices

Help them define their requirements and assess viable logging tools

Help them upgrade their current tool to take advantage of any new features

NO.161 One of your primary business objectives is being able to trust the data stored in your application. You want to log all changes to the application data. How can you design your logging system to verify authenticity of your logs?

Write the log concurrently in the cloud and on premises.

Use a SQL database and limit who can modify the log table.

Digitally sign each timestamp and log entry and store the signature.

Create a JSON dump of each log entry and store it in Google Cloud Storage.

NO.162 You need to reduce the number of unplanned rollbacks of erroneous production deployments in your
company’s web hosting platform. Improvement to the QA/Test processes accomplished an 80% reduction.
Which additional two approaches can you take to further reduce the rollbacks? Choose 2 answers.

Introduce a green-blue deployment model

Replace the QA environment with canary releases

Fragment the monolithic platform into microservices

Reduce the platform’s dependency on relational database systems

Replace the platform’s relational database systems with a NoSQL database

NO.163 Your team will start developing a new application using microservices architecture on Kubernetes Engine. As part of the development lifecycle, any code change that has been pushed to the remote develop branch on your GitHub repository should be built and tested automatically. When the build and test are successful, the relevant microservice will be deployed automatically in the development environment. You want to ensure that all code deployed in the development environment follows this process. What should you do?

Have each developer install a pre-commit hook on their workstation that tests the code and builds the container when committing on the development branch.
After a successful commit, have the developer deploy the newly built container image on the development cluster.

Install a post-commit hook on the remote git repository that tests the code and builds the container when code is pushed to the development branch.
After a successful commit, have the developer deploy the newly built container image on the development cluster.

Create a Cloud Build trigger based on the development branch that tests the code, builds the container, and stores it in Container Registry.
Create a deployment pipeline that watches for new images and deploys the new image on the development cluster. Ensure only the deployment tool has access to deploy new versions.

Create a Cloud Build trigger based on the development branch to build a new container image and store it in Container Registry.
Rely on Vulnerability Scanning to ensure the code tests succeed.
As the final step of the Cloud Build process, deploy the new container image on the development cluster.
Ensure only Cloud Build has access to deploy new versions.

NO.164 You want to make a copy of a production Linux virtual machine in the US-Central region. You want to manage and replace the copy easily if there are changes on the production virtual machine. You will deploy the copy as a new instances in a different project in the US-East region. What steps must you take?

Use the Linux dd and netcat command to copy and stream the root disk contents to a new virtual machine instance in the US-East region.

Create a snapshot of the root disk and select the snapshot as the root disk when you create a new virtual machine instance in the US-East region.

Create an image file from the root disk with Linux dd command, create a new disk from the image file, and use it to create a new virtual machine instance in the US-East region

Create a snapshot of the root disk, create an image file in Google Cloud Storage from the snapshot, and create a new virtual machine instance in the US-East region using the image file for the root disk.

NO.165 Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?

Load data into Google BigQuery.

Insert data into Google Cloud SQL.

Put flat files into Google Cloud Storage.

Stream data into Google Cloud Datastore.

NO.166 Your company has decided to build a backup replica of their on-premises user authentication PostgreSQL database on Google Cloud Platform. The database is 4 TB, and large updates are frequent. Replication requires private address space communication. Which networking approach should you use?

Google Cloud Dedicated Interconnect

Google Cloud VPN connected to the data center network

A NAT and TLS translation gateway installed on-premises

A Google Compute Engine instance with a VPN server installed connected to the data center network

NO.167 The JencoMart security team requires that all Google Cloud Platform infrastructure is deployed using a least privilege model with separation of duties for administration between production and development resources.
What Google domain and project structure should you recommend?

Create two G Suite accounts to manage users: one for development/test/staging and one for production.
Each account should contain one project for every application

Create two G Suite accounts to manage users: one with a single project for all development applications and one with a single project for all production applications

Create a single G Suite account to manage users with each stage of each application in its own project

Create a single G Suite account to manage users with one project for the development/test/staging environment and one project for the production environment

NO.168 You have been asked to select the storage system for the click-data of your company’s large portfolio of websites. This data is streamed in from a custom website analytics package at a typical rate of 6,000 clicks per minute, with bursts of up to 8,500 clicks per second. It must been stored for future analysis by your data science and user experience teams. Which storage infrastructure should you choose?

Google Cloud SQL

Google Cloud Bigtable

Google Cloud Storage

Google cloud Datastore

NO.169 Case Study: 9 – Helicopter Racing League
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
– Existing content is stored in an object storage service on their existing public cloud provider.
– Video encoding and transcoding is performed on VMs created for each job.
– Race predictions are performed using TensorFlow running on VMs in the current public cloud
provider.
Business requirements
HRL’s owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
– Support ability to expose the predictive models to partners.
– Increase predictive capabilities during and before races:
*Race results
*Mechanical failures
*Crowd sentiment
– Increase telemetry and create additional insights.
– Measure fan engagement with new predictions.
– Enhance global availability and quality of the broadcasts.
– Increase the number of concurrent viewers.
– Minimize operational complexity.
– Ensure compliance with regulations.
– Create a merchandising revenue stream.
Technical requirements
– Maintain or increase prediction throughput and accuracy.
– Reduce viewer latency.
– Increase transcoding performance.
– Create real-time analytics of viewer consumption patterns and engagement.
– Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
For this question, refer to the Helicopter Racing League (HRL) case study. Recently HRL started a new regional racing league in Cape Town, South Africa. In an effort to give customers in Cape Town a better user experience, HRL has partnered with the Content Delivery Network provider, Fastly. HRL needs to allow traffic coming from all of the Fastly IP address ranges into their Virtual Private Cloud network (VPC network). You need to configure the update that will allow only the Fastly IP address ranges through the External HTTP(S) load balancer. Which should you do?

Apply a Cloud Armor security policy to external load balancers using a named IP list for Fastly.

Apply a Cloud Armor security policy to external load balancers using the IP addresses that Fastly has published.

Apply a VPC firewall rule on port 443 for Fastly IP address ranges.

Apply a VPC firewall rule on port 443 for network resources tagged with sourceiplist-fastly.

NO.170 The application reliability team at your company has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most
15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss.
Which process should you implement?

* Append metadata to file body.
* Compress individual files.
* Name files with serverName-Timestamp.
* Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket.
Otherwise, save files to existing bucket

* Batch every 10,000 events with a single manifest file for metadata.
* Compress event files and manifest file into a single archive file.
* Name files using serverName-EventSequence.
* Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket.
Otherwise, save the single archive file to existing bucket.

* Compress individual files.
* Name files with serverName-EventSequence.
* Save files to one bucket
* Set custom metadata headers for each object after saving.

* Append metadata to file body.
* Compress individual files.
* Name files with a random prefix pattern.
* Save files to one bucket

NO.171 Case Study: 3 – JencoMart Case Study
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company’s core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world’s leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S.
data centers.
Database
* Oracle Database stores user profiles

* PostgreSQL database stores user credentials
-homed in US West

service level agreement (SLA)

Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:

* 20 machines in US East Coast, each machine has:
-core CPU

Storage
* Access to shared 100 TB SAN in each location
* Tape backup every week
Business Requirements
* Optimize for capacity during peak periods and value during off-peak periods
* Guarantee service availably and support
* Reduce on-premises footprint and associated financial and environmental impact.
* Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase
* Expand services into Asia.
Technical Requirements
* Assess key application for cloud suitability.
* Modify application for the cloud.
* Move applications to a new infrastructure.
* Leverage managed services wherever feasible
* Sunset 20% of capacity in existing data centers
* Decrease latency in Asia
CEO Statement
JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large global company, we also have a responsibility to the environment through ‘green’ initiatives and polices.
CTO Statement
The challenges of operating data centers prevents focus on key technologies critical to our long- term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service customers.
CFO Statement
Since its founding JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long- term success. This model will allow us to respond to increasing customer demand during peak and reduce costs.
For this question, refer to the JencoMart case study.
JencoMart has decided to migrate user profile storage to Google Cloud Datastore and the application servers to Google Compute Engine (GCE). During the migration, the existing infrastructure will need access to Datastore to upload the data. What service account key- management strategy should you recommend?

Provision service account keys for the on-premises infrastructure and for the GCE virtual machines (VMs).

Authenticate the on-premises infrastructure with a user account and provision service account keys for the VMs.

Provision service account keys for the on-premises infrastructure and use Google Cloud Platform (GCP) managed keys for the VMs

Deploy a custom authentication service on GCE/Google Container Engine (GKE) for the on- premises infrastructure and use GCP managed keys for the VMs.

NO.172 You deploy your custom Java application to Google App Engine. It fails to deploy and gives you the following stack trace.

What should you do?

Upload missing JAR files and redeploy your application.

Digitally sign all of your JAR files and redeploy your application

Recompile the CLoakedServlet class using and MD5 hash instead of SHA1

NO.173 One of the developers on your team deployed their application in Google Container Engine with the Dockerfile below. They report that their application deployments are taking too long.

You want to optimize this Dockerfile for faster deployment times without adversely affecting the app’s functionality.
Which two actions should you take? (Choose two.)

Remove Python after running pip

Remove dependencies from requirements.txt

Use a slimmed-down base image like Alpine Linux

Use larger machine types for your Google Container Engine node pools

Copy the source after he package dependencies (Python and pip) are installed

NO.174 Your company wants to try out the cloud with low risk. They want to archive approximately 100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long-term disaster recovery backup. Which two steps should they take? Choose 2 answers

Load logs into Google BigQuery.

Load logs into Google Cloud SQL.

Import logs into Google Stackdriver.

Insert logs into Google Cloud Bigtable.

Upload log files into Google Cloud Storage.

NO.175 You have developed a non-critical update to your application that is running in a managed instance group, and have created a new instance template with the update that you want to release. To prevent any possible impact to the application, you don’t want to update any running instances. You want any new instances that are created by the managed instance group to contain the new update. What should you do?

Start a new rolling restart operation.

Start a new rolling replace operation.

Start a new rolling update. Select the Proactive update mode.

Start a new rolling update. Select the Opportunistic update mode.

NO.176 You have an App Engine application that needs to be updated. You want to test the update with production traffic before replacing the current application version.
What should you do?

Deploy the update using the Instance Group Updater to create a partial rollout, which allows for canary testing.

Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.

Deploy the update in a new VPC, and use Google’s global HTTP load balancing to split traffic between the update and current applications.

Deploy the update as a new App Engine application, and use Google’s global HTTP load balancing to split traffic between the new and current applications.

NO.177 Your company wants to try out the cloud with low risk. They want to archive approximately 100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long-term disaster recovery backup. Which two steps should they take?
Choose 2 answers

Load logs into Google BigQuery.

Load logs into Google Cloud SQL.

Import logs into Google Stackdriver.

Insert logs into Google Cloud Bigtable.

Upload log files into Google Cloud Storage.