[Jun-2024] Check your preparation for Google Professional-Cloud-Architect On-Demand Exam
Practice Exam Professional-Cloud-Architect Realistic Dumps Verified Questions
What is the duration, language, and format of Google Professional Cloud Architect Exam
- Length of Examination: 120 minutes
- Language: Google Professional Cloud Architect offered in English (U.S.), Japanese, Spanish, and Portuguese
- Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP
- Passing score: 80%
- Format: Multiple choices, multiple answers
Designing for Compliance and Security
- Design for security: the learners need to gain knowledge of penetration testing; identity and access management; resource hierarchy; separation of duties; security control; data security; customer-managed encryption key management with Cloud KMS;
- Design for compliance: this covers one’s knowledge of legislation (health record privacy, data privacy, ownership, and children’s privacy); commercial (sensitive data handling and personally identifiable information); industry certifications (SOC 2); audits (such as logs).
Google Professional-Cloud-Architect Certification Exam is a valuable certification for individuals who want to demonstrate their expertise in cloud architecture and GCP. It is a challenging exam that requires candidates to have a strong understanding of GCP services and infrastructure. By passing Professional-Cloud-Architect exam, candidates can prove their knowledge and skills in designing, developing, and managing secure, scalable, and reliable cloud solutions using GCP.
NEW QUESTION # 126
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 its system architecture to multiple locations when Google cloud platform.
Which approach should they use?
- A. 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.
- B. Use regional managed instance groups and a global load balancer to increase reliability by providing automatic failover between zones in different regions.
- C. 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.
- D. 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.
Answer: A
Explanation:
Explanation/Reference:
Dress4Win, B
Testlet 1
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a webapp and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4Win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
* MySQL. 1 server for user data, inventory, static data:
- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
* Redis 3 server cluster for metadata, social graph, caching. Each server is:
- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
* 40 Web Application servers providing micro-services based APIs and static content.
- Tomcat - Java
- Nginx
- 4 core CPUs
- 32 GB of RAM
* 20 Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUS
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
* 3 RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM
* Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
* iSCSI for VM hosts
* Fiber channel SAN - MySQL databases
- 1 PB total storage; 400 TB available
* NAS - image storage, logs, backups
- 100 TB total storage; 35 TB available
Business Requirements
* Build a reliable and reproducible environment with scaled parity of production.
* Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
* Improve business agility and speed of innovation through rapid provisioning of new resources.
* Analyze and optimize architecture for performance in the cloud.
Technical Requirements
* Easily create non-production environment in the cloud.
* Implement an automation framework for provisioning resources in cloud.
* Implement a continuous deployment process for deploying applications to the on-premises datacenter or cloud.
* Support failover of the production environment to cloud during an emergency.
* Encrypt data on the wire and at rest.
* Support multiple private connections between the production data center and cloud environment.
Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
NEW QUESTION # 127
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 2 answers.
- A. Remove Python after running pip.
- B. Remove dependencies from requirements.txt.
- C. Copy the source after the package dependencies (Python and pip) are installed.
- D. Use a slimmed-down base image like Alpine linux.
- E. Use larger machine types for your Google Container Engine node pools.
Answer: C,D
Explanation:
Explanation
The speed of deployment can be changed by limiting the size of the uploaded app, limiting the complexity of the build necessary in the Dockerfile, if present, and by ensuring a fast and reliable internet connection.
Note: Alpine Linux is built around musl libc and busybox. This makes it smaller and more resource efficient than traditional GNU/Linux distributions. A container requires no more than 8 MB and a minimal installation to disk requires around 130 MB of storage. Not only do you get a fully-fledged Linux environment but a large selection of packages from the repository.
References: https://groups.google.com/forum/#!topic/google-appengine/hZMEkmmObDU
https://www.alpinelinux.org/about/
NEW QUESTION # 128
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?
- A. Authenticate the on-premises infrastructure with a user account and provision service account keys for the VMs.
- B. Provision service account keys for the on-premises infrastructure and use Google Cloud Platform (GCP) managed keys for the VMs
- C. Deploy a custom authentication service on GCE/Google Container Engine (GKE) for the on-premises infrastructure and use GCP managed keys for the VMs.
- D. Provision service account keys for the on-premises infrastructure and for the GCE virtual machines (VMs).
Answer: D
Explanation:
Explanation
https://cloud.google.com/iam/docs/understanding-service-accounts
Migrating data to Google Cloud Platform
Let's say that you have some data processing that happens on another cloud provider and you want to transfer the processed data to Google Cloud Platform. You can use a service account from the virtual machines on the external cloud to push the data to Google Cloud Platform. To do this, you must create and download a service account key when you create the service account and then use that key from the external process to call the Cloud Platform APIs.
References:
https://cloud.google.com/iam/docs/understanding-service-accounts#migrating_data_to_google_cloud_platform
NEW QUESTION # 129
TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to
20 million 600 byte records a second for 40 TB an hour.
How should you design the data ingestion?
- A. Vehicles continue to write data using the existing system (FTP)
- B. Vehicles write data directly to GCS
- C. Vehicles stream data directly to Google BigQuery
- D. Vehicles write data directly to Google Cloud Pub/Sub
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Streamed data is available for real-time analysis within a few seconds of the first streaming insertion into a
table.
Instead of using a job to load data into BigQuery, you can choose to stream your data into BigQuery one
record at a time by using the tabledata().insertAll() method. This approach enables querying data without
the delay of running a load job.
References: https://cloud.google.com/bigquery/streaming-data-into-bigquery
NEW QUESTION # 130
Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize operations. They do not have any existing code for this analysis, so they are exploring all their options.
These options include a mix of batch and stream processing, as they are running some hourly jobs and live- processing some data as it comes in.
Which technology should they use for this?
- A. Google Cloud Dataproc
- B. Google Cloud Dataflow
- C. Google Container Engine with Bigtable
- D. Google Compute Engine with Google BigQuery
Answer: B
Explanation:
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed.
Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 131
You want to enable your running Google Container Engine cluster to scale as demand for your application changes.
What should you do?
- A. Update the existing Container Engine cluster with the following command:
gcloud alpha container clusters update mycluster --enable-autoscaling --min-nodes=1 --max-nodes=10 - B. Add additional nodes to your Container Engine cluster using the following command:
gcloud container clusters resize CLUSTER_NAME --size 10 - C. Add a tag to the instances in the cluster with the following command:
gcloud compute instances add-tags INSTANCE --tags enable --autoscaling max-nodes-10 - D. Create a new Container Engine cluster with the following command:
gcloud alpha container clusters create mycluster --enable-autocaling --min-nodes=1 --max-nodes=10
Answer: C
Explanation:
and redeploy your application.
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler Cluster autoscaling
--enable-autoscaling
Enables autoscaling for a node pool.
Enables autoscaling in the node pool specified by --node-pool or the default node pool if --node-pool is not provided.
Where:
--max-nodes=MAX_NODES
Maximum number of nodes in the node pool.
Maximum number of nodes to which the node pool specified by --node-pool (or default node pool if unspecified) can scale.
Incorrect Answers:
C, D: Warning: Do not use Alpha Clusters or alpha features for production workloads.
Note: You can experiment with Kubernetes alpha features by creating an alpha cluster. Alpha clusters are short-lived clusters that run stable Kubernetes releases with all Kubernetes APIs and features enabled. Alpha clusters are designed for advanced users and early adopters to experiment with workloads that take advantage of new features before those features are production-ready. You can use Alpha clusters just like normal Kubernetes Engine clusters.
NEW QUESTION # 132
Your customer runs a web service used by e-commerce sites to offer product recommendations to users. the company has begun experimenting with a machine learning model on Google Cloud Platform to improve the quality of results.
What should the customer do to improve their model's results over time?
- A. Export Cloud Machine Learning Engine performance metrics from Stackdriver to BigQuery, to be used to analyze the efficiency of the model.
- B. Save a history of recommendations and results of the recommendations in BigQuery, to be used as training data.
- C. Monitor Compute Engine announcements for availability of newer CPU architectures, and deploy the model to them as soon as they are available for additional performance.
- D. Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which offer better results.
Answer: B
Explanation:
BigQuery need data to be applied in AI.
NEW QUESTION # 133
You have a Python web application with many dependencies that requires 0.1 CPU cores and
128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?
- A. Perform the following:
1. Create a Kubernetes Engine cluster with n1-standard-4 type machines.
2. Build a Docker image from the master branch will all of the dependencies, and tag it with
"latest".
3. Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to
"Always". Restart the pods to automatically deploy new production releases. - B. Perform the following:
1. Create a managed instance group with f1-micro type machines.
2. Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3. Restart the instances to automatically deploy new production releases. - C. Perform the following:
1. Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2. Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3. Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing. - D. Perform the following:
1. Create a managed instance group with n1-standard-1 type machines.
2. Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
3. Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
Answer: D
NEW QUESTION # 134
For this question, refer to the TerramEarth case study. You are asked to design a new architecture for the
ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow
Google-recommended practices.
Considering the technical requirements, which components should you use for the ingestion of the data?
- A. Google Kubernetes Engine with an SSL Ingress
- B. Compute Engine with specific SSH keys
- C. Compute Engine with project-wide SSH keys
- D. Cloud IoT Core with public/private key pairs
Answer: A
Explanation:
Explanation/Reference:
Testlet 1
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe
using a website and mobile application. The company also cultivates an active social network that
connects their users with designers and retailers. They monetize their services through advertising, e-
commerce, referrals, and a premium app model.
Company Background
Dress4win's application has grown from a few servers in the founder's garage to several hundred servers
and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient
for the application's rapid growth. Because of this growth and the company's desire to innovate faster,
Dress4win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and
test environments. They are also considering building a disaster recovery site, because their current
infrastructure is at a single location. They are not sure which components of their architecture they can
migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location.
Databases:
- MySQL - user data, inventory, static data
- Redis - metadata, social graph, caching
Application servers:
- Tomcat - Java micro-services
- Nginx - static content
- Apache Beam - Batch processing
Storage appliances:
- iSCSI for VM hosts
- Fiber channel SAN - MySQL databases
- NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
MQ servers:
- Messaging
- Social notifications
- Events
Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
Business Requirements
Build a reliable and reproducible environment with scaled parity of production.
Improve security by defining and adhering to a set of security and Identity and Access Management
(IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud.
Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud.
Support failover of the production environment to cloud during an emergency.
Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure.
They are also concerned that a new competitor could use a public cloud platform to offset their up-front
investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end
of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new
projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80%
of our capacity is sitting idle.
CFO Statement
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause
an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our
total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50%
lower than our current model.
NEW QUESTION # 135
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. 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. - B. 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. - C. 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. - 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.
Answer: B
NEW QUESTION # 136
You are managing an application deployed on Cloud Run for Anthos, and you need to define a strategy for deploying new versions of the application. You want to evaluate the new code with a subset of production traffic to decide whether to proceed with the rollout. What should you do?
- A. Deploy a new revision to Cloud Run with the new version. Configure traffic percentage between revisions.
- B. In the Google Cloud Console, configure Traffic Director with a new Service that points to the new version of the application on Cloud Run. Configure Traffic Director to send a small percentage of traffic to the new version of the application.
- C. In the Google Cloud Console page for Cloud Run, set up continuous deployment using Cloud Build for the development branch. As part of the Cloud Build trigger, configure the substitution variable TRAFFIC_PERCENTAGE with the percentage of traffic you want directed to a new version.
- D. Deploy a new service to Cloud Run with the new version. Add a Cloud Load Balancing instance in front of both services.
Answer: C
NEW QUESTION # 137
Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs. What should they do?
- A. Have the old API forward traffic to the new API based on the path.
- B. Reconfigure old clients to use a new endpoint for the new API.
- C. Use separate backend pools for each API path behind the load balancer.
- D. Configure a new load balancer for the new version of the API.
Answer: D
NEW QUESTION # 138
Your company is migrating its on-premises data center into the cloud. As part of the migration, you want to integrate Kubernetes Engine for workload orchestration. Parts of your architecture must also be PCI DSScompliant.
Which of the following is most accurate?
- A. App Engine is the only compute platform on GCP that is certified for PCI DSS hosting.
- B. Kubernetes Engine cannot be used under PCI DSS because it is considered shared hosting.
- C. Kubernetes Engine and GCP provide the tools you need to build a PCI DSS-compliant environment.
- D. All Google Cloud services are usable because Google Cloud Platform is certified PCI-compliant.
Answer: D
Explanation:
Reference:
https://cloud.google.com/security/compliance/pci-dss
NEW QUESTION # 139
You deploy your custom java application to google app engine.
It fails to deploy and gives you the following stack trace:
- A. Recompile the CLoakedServlet class using and MD5 hash instead of SHA1
- B. Upload missing JAR files and redeploy your application
- C. Digitally sign all of your JAR files and redeploy your application.
Answer: C
NEW QUESTION # 140
You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine.
The script is printing errors that it cannot connect to BigQuery.
What should you do to fix the script?
- A. Install the latest BigQuery API client library for Python
- B. Run your script on a new virtual machine with the BigQuery access scope enabled
- C. Install the bq component for gcloud with the command gcloud components install bq.
- D. Create a new service account with BigQuery access and execute your script with that user
Answer: B
NEW QUESTION # 141
You are developing an application using different microservices that should remain internal to the cluster. You want to be able to configure each microservice with a specific number of replicas. You also want to be able to address a specific microservice from any other microservice in a uniform way, regardless of the number of replicas the microservice scales to. You need to implement this solution on Google Kubernetes Engine. What should you do?
- A. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using a Service, and use the Service DNS name to address it from other microservices within the cluster.
- B. Deploy each microservice as a Pod. Expose the Pod in the cluster using an Ingress, and use the Ingress IP address name to address the Pod from other microservices within the cluster.
- C. Deploy each microservice as a Pod. Expose the Pod in the cluster using a Service, and use the Service DNS name to address the microservice from other microservices within the cluster.
- D. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using an Ingress, and use the Ingress IP address to address the Deployment from other microservices within the cluster.
Answer: A
Explanation:
https://kubernetes.io/docs/concepts/services-networking/ingress/
NEW QUESTION # 142
For this question, refer to the EHR Healthcare case study. You need to define the technical architecture for securely deploying workloads to Google Cloud. You also need to ensure that only verified containers are deployed using Google Cloud services. What should you do? (Choose two.)
- A. Configure Container Registry to only allow trusted service accounts to create and deploy containers from the registry.
- B. Configure Jenkins to utilize Kritis to cryptographically sign a container as part of a CI/CD pipeline.
- C. Configure Container Registry to use vulnerability scanning to confirm that there are no vulnerabilities before deploying the workload.
- D. Enable Binary Authorization on GKE, and sign containers as part of a CI/CD pipeline.
Answer: C,D
Explanation:
Binary Authorization to ensure only verified containers are deployed To ensure deployment are secure and and consistent, automatically scan images for vulnerabilities with container analysis (https://cloud.google.com/docs/ci-cd/overview?hl=en&skip_cache=true)
NEW QUESTION # 143
You have an application deployed on Kubernetes Engine using a Deployment named echo-deployment. The deployment is exposed using a Service called echo-service. You need to perform an update to the application with minimal downtime to the application. What should you do?
- A. Update the service yaml file which the new container image. Use kubect1 delete service/echoservice and kubect1 create -f <yaml-file>
- B. Update the deployment yaml file with the new container image. Use kubect1 delete deployment/ echo-deployment and kubect1 create -f <yaml-file>
- C. Use kubect1 set image deployment/echo-deployment <new-image>
- D. Use the rolling update functionality of the Instance Group behind the Kubernetes cluster
Answer: C
Explanation:
Reference:
https://cloud.google.com/kubernetes-engine/docs/how-to/updating-apps#updating_an_application
NEW QUESTION # 144
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