2026 100% Free Professional-Cloud-Architect Daily Practice Exam With 378 Questions
Professional-Cloud-Architect exam torrent Google study guide
NEW QUESTION # 46
Case Study: 2 - TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day.
TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
- Decrease unplanned vehicle downtime to less than 1 week, without
increasing the cost of carrying surplus inventory
- Support the dealer network with more data on how their customers use
their equipment IP better position new products and services.
- Have the ability to partner with different companies-especially with
seed and fertilizer suppliers in the fast-growing agricultural
business-to create compelling joint offerings for their customers
CEO Statement
We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customers' wait time for parts You decided to focus on reduction of the 3 weeks aggregate reporting time Which modifications to the company's processes should you recommend?
- A. Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics.
- B. Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics.
- C. Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor.
- D. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics.
Answer: D
Explanation:
The Avro binary format is the preferred format for loading compressed data. Avro data is faster to load because the data can be read in parallel, even when the data blocks are compressed.
Cloud Storage supports streaming transfers with the gsutil tool or boto library, based on HTTP chunked transfer encoding. Streaming data lets you stream data to and from your Cloud Storage account as soon as it becomes available without requiring that the data be first saved to a separate file. Streaming transfers are useful if you have a process that generates data and you do not want to buffer it locally before uploading it, or if you want to send the result from a computational pipeline directly into Cloud Storage.
References: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data
NEW QUESTION # 47
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files The database is about to run out of storage space How can you remediate the problem with the least amount of downtime?
- A. In the Cloud Platform Console, create a snapshot of the persistent disk, restore the snapshot to a new larger disk, unmount the old disk, mount the new disk, and restart the database service.
- B. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine.
- C. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.
- D. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk.
- E. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux.
Answer: C
Explanation:
On Linux instances, connect to your instance and manually resize your partitions and file systems to use the additional disk space that you added.
Extend the file system on the disk or the partition to use the added space. If you grew a partition on your disk, specify the partition. If your disk does not have a partition table, specify only the disk ID.
sudo resize2fs /dev/[DISK_ID][PARTITION_NUMBER]
where [DISK_ID] is the device name and [PARTITION_NUMBER] is the partition number for the device where you are resizing the file system.
NEW QUESTION # 48
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?
- A. * Compress individual files.
* Name files with serverName-EventSequence.
* Save files to one bucket
* Set custom metadata headers for each object after saving. - B. * Append metadata to file body.
* Compress individual files.
* Name files with a random prefix pattern.
* Save files to one bucket - C. * 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 - D. * 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.
Answer: B
Explanation:
In order to maintain a high request rate, avoid using sequential names. Using completely random object names will give you the best load distribution. Randomness after a common prefix is effective under the prefix https://cloud.google.com/storage/docs/request-rate
NEW QUESTION # 49
Your company has a project in Google Cloud with three Virtual Private Clouds (VPCs). There is a Compute Engine instance on each VPC. Network subnets do not overlap and must remain separated. The network configuration is shown below.
Instance #1 is an exception and must communicate directly with both Instance #2 and Instance #3 via internal IPs. How should you accomplish this?
- A. Create a cloud router to advertise subnet #2 and subnet #3 to subnet #1.
- B. Create two VPN tunnels via CloudVPN:
* 1 between VPC #1 and VPC #2.
* 1 between VPC #2 and VPC #3.
Update firewall rules to enable traffic between the instances. - C. Add two additional NICs to Instance #1 with the following configuration:
* NIC1
* VPC: VPC #2
* SUBNETWORK: subnet #2
* NIC2
* VPC: VPC #3
* SUBNETWORK: subnet #3
Update firewall rules to enable traffic between instances. - D. Peer all three VPCs:
* Peer VPC #1 with VPC #2.
* Peer VPC #2 with VPC #3.
Update firewall rules to enable traffic between the instances.
Answer: C
NEW QUESTION # 50
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?
- A. Load data into Google BigQuery.
- B. Insert data into Google Cloud SQL.
- C. Stream data into Google Cloud Datastore.
- D. Put flat files into Google Cloud Storage.
Answer: A
Explanation:
BigQuery is Google's serverless, highly scalable, low cost enterprise data warehouse designed to make all your data analysts productive. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL and you don't need a database administrator.
BigQuery enables you to analyze all your data by creating a logical data warehouse over managed, columnar storage as well as data from object storage, and spreadsheets.
References: https://cloud.google.com/bigquery/
Reference:
Google Big Query is for multi peta byte storage , HA(High availability) which means 24 hours, SQL interface .
https://medium.com/google-cloud/the-12-components-of-google-bigquery-c2b49829a7c7
https://cloud.google.com/solutions/bigquery-data-warehouse
https://cloud.google.com/bigquery/
NEW QUESTION # 51
Your organization has a 3-tier web application deployed in the same network on Google Cloud Platform.
Each tier (web, API, and database) scales independently of the others. Network traffic should flow through the web to the API tier and then on to the database tier. Traffic should not flow between the web and the database tier.
How should you configure the network?
- A. Add tags to each tier and set up firewall rules to allow the desired traffic flow
- B. Add each tier to a different subnetwork
- C. Set up software based firewalls on individual VMs
- D. Add tags to each tier and set up routes to allow the desired traffic flow
Answer: A
Explanation:
Google Cloud Platform(GCP) enforces firewall rules through rules and tags. GCP rules and tags can be defined once and used across all regions.
Reference: https://cloud.google.com/docs/compare/openstack/
https://aws.amazon.com/it/blogs/aws/building-three-tier-architectures-with-security-groups/
NEW QUESTION # 52
Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily.
You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user's perspective. What should you do?
- A. Create CPU Utilization and Request Latency as service level indicators.
- B. Create Request Latency and Error Rate as service level indicators.
- C. Create Server Uptime and Error Rate as service level indicators.
- D. Create GKE CPU Utilization and Memory Utilization as service level indicators.
Answer: B
Explanation:
Topic 10, 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.
NEW QUESTION # 53
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.
Answer: D
NEW QUESTION # 54
You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore. What should you do?
- A. Upload the configuration file the App Engine's default Cloud Storage bucket, and have App Engine detect the new indexes
- B. Create an HTTP request to the built-in python module to send the index configuration file to your application
- C. Point gcloud datastore create-indexes to your configuration file
- D. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file
Answer: C
Explanation:
You upload your index.yaml configuration file to Datastore with the gcloud command. If the index.yaml file defines any indexes that don't exist in Datastore, those new indexes are built.
gcloud datastore indexes create index.yaml
https://cloud.google.com/sdk/gcloud/reference/datastore/indexes/create
NEW QUESTION # 55
Your company just finished a rapid lift and shift to Google Compute Engine for your compute needs. You have another 9 months to design and deploy a more cloud-native solution.
Specifically, you want a system that is no-ops and auto-scaling. Which two compute products should you choose? Choose 2 answers
- A. Google Container Engine with containers
- B. Compute Engine with containers
- C. Google App Engine Standard Environment
- D. Compute Engine with managed instance groups
- E. Compute Engine with custom instance types
Answer: A,C
Explanation:
B: With Container Engine, Google will automatically deploy your cluster for you, update, patch, secure the nodes.
Kubernetes Engine's cluster autoscaler automatically resizes clusters based on the demands of the workloads you want to run.
C: Solutions like Datastore, BigQuery, AppEngine, etc are truly NoOps.
App Engine by default scales the number of instances running up and down to match the load, thus providing consistent performance for your app at all times while minimizing idle instances and thus reducing cost.
Note: At a high level, NoOps means that there is no infrastructure to build out and manage during usage of the platform. Typically, the compromise you make with NoOps is that you lose control of the underlying infrastructure.
References: https://www.quora.com/How-well-does-Google-Container-Engine-support-Google- Cloud-Platform%E2%80%99s-NoOps-claim
NEW QUESTION # 56
For this question, refer to the TerramEarth case study.
To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections. What should you do?
- A. Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in us, eu, and asia. Run the ETL process using the data in the bucket.
- B. Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket.
- C. Directly transfer the files to a different Google Cloud Regional Storage bucket location in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket.
- D. Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket.
Answer: C
Explanation:
Explanation
https://cloud.google.com/storage/docs/locations
NEW QUESTION # 57
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?
- A. * Compress individual files.
* Name files with serverName-EventSequence.
* Save files to one bucket
* Set custom metadata headers for each object after saving. - B. * 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 - C. * 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. - D. * Append metadata to file body.
* Compress individual files.
* Name files with a random prefix pattern.
* Save files to one bucket
Answer: B
NEW QUESTION # 58
Your product team is building a critical, customer-facing application on Google Cloud. The development team wants to use Spanner for their database to take advantage of its horizontal scalability and low operational overhead However, the FinOps team is concerned about the direct monthly cost of Spanner and proposed using a self-managed PostgreSQL database on Compute Engine VMs instead. You need to resolve this conflict and ensure the project moves forward with an architecturally sound database choice that balances technical requirements with financial constraints. What should you do?
- A. Cite the reliability and performance optimization pillars of the Google Cloud Well-Architected Framework to formally justify the use of Spanner.
- B. Suggest using Cloud SQL for PostgreSQL as a compromise to get a managed service at a lower cost than Spanner.
- C. Provide the development team with a reference architecture for deploying a highly available PostgreSQL cluster on a regional managed instance group (MIG).
- D. Develop a total cost of ownership (TCO) analysis that includes operational overhead, and present it in a workshop to facilitate a decision.
Answer: D
Explanation:
This is a stakeholder and trade-off decision, not just a pure tech choice. The Google- recommended approach is to perform a total cost of ownership (TCO) analysis that includes not only direct service costs, but also operational overhead, maintenance, reliability engineering, and scaling complexity of a self-managed PostgreSQL on Compute Engine vs. managed options like Spanner. Presenting this TCO in a workshop with development and FinOps enables a data- driven, shared decision that balances technical requirements (scalability, reliability) with financial constraints, ensuring an architecturally sound and agreed-upon database choice.
NEW QUESTION # 59
Case Study: 4 - Dress4Win case study
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 freemium 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 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 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.
For this question, refer to the Dress4Win case study.
The Dress4Win security team has disabled external SSH access into production virtual machines (VMs) on Google Cloud Platform (GCP). The operations team needs to remotely manage the VMs, build and push Docker containers, and manage Google Cloud Storage objects. What can they do?
- A. Develop a new access request process that grants temporary SSH access to cloud VMs when an operations engineer needs to perform a task.
- B. Have the development team build an API service that allows the operations team to execute specific remote procedure calls to accomplish their tasks.
- C. Grant the operations engineers access to use Google Cloud Shell.
- D. Configure a VPN connection to GCP to allow SSH access to the cloud VMs.
Answer: C
NEW QUESTION # 60
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. Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which offer better results.
- B. 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.
- C. Save a history of recommendations and results of the recommendations in BigQuery, to be used as training data.
- D. Export Cloud Machine Learning Engine performance metrics from Stackdriver to BigQuery, to be used to analyze the efficiency of the model.
Answer: A
NEW QUESTION # 61
You are designing a new insurance claims processing application that will be deployed on Google Kubernetes Engine (GKE) Your company's compliance team requires a complete and non- repudiable audit trail for all administrative actions from day one. Your application must capture who deploys a new container image, who modifies the GKE cluster's configuration, and who interacts with running pods or Kubernetes secrets using kubectl. What should you do?
- A. Deploy a DaemonSet to every node in the GKE cluster that runs a logging agent to collect and forward all container logs to Cloud Logging.
- B. Activate the Security Command Center Premium tier to analyze GKE logs and detect threats, vulnerabilities, and misconfigurations in real time.
- C. Enable GKE Audit Logging to send Kubernetes API server logs to Cloud Logging, and ensure Cloud Audit Logs are enabled for the project.
- D. Enable Binary Authorization on the GKE cluster, and create a policy that requires all deployed container images to be signed by a trusted attestor.
Answer: C
Explanation:
Enabling GKE Audit Logging records all interactions with the Kubernetes API server (such as deployments, config changes, kubectl exec/port-forward/secret access), including who performed each action and what they did. Combined with Cloud Audit Logs at the project level, this provides a complete, tamper-resistant, and non-repudiable audit trail of all administrative operations from day one, satisfying the compliance requirements.
NEW QUESTION # 62
You want your Google Kubernetes Engine cluster to automatically add or remove nodes based on CPUload.
What should you do?
- A. Configure a HorizontalPodAutoscaler with a target CPU usage. Enable autoscaling on the managed instance group for the cluster using the gcloud command.
- B. Configure a HorizontalPodAutoscaler with a target CPU usage. Enable the Cluster Autoscaler from the GCP Console.
- C. Create a deployment and set the maxUnavailable and maxSurge properties. Enable the Cluster Autoscaler using the gcloud command.
- D. Create a deployment and set the maxUnavailable and maxSurge properties. Enable autoscaling on the cluster managed instance group from the GCP Console.
Answer: A
NEW QUESTION # 63
You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading. Where should you store the data?
- A. Google Cloud Storage
- B. Google BigQuery
- C. Google Cloud SQL
- D. Google Cloud Bigtable
Answer: D
NEW QUESTION # 64
Your architecture calls for the centralized collection of all admin activity and VM system logs within your project. How should you collect these logs from both VMs and services?
- A. All admin and VM system logs are automatically collected by Stackdriver.
- B. Install the Stackdriver Logging agent on a single compute instance and let it collect all audit and access logs for your environment.
- C. Launch a custom syslogd compute instance and configure your GCP project and VMs to forward all logs to it.
- D. Stackdriver automatically collects admin activity logs for most services. The Stackdriver Logging agent must be installed on each instance to collect system logs.
Answer: D
Explanation:
Admin and event logs are configured by default. VM System logs require a logging agent to be configured.
https://cloud.google.com/logging/docs/agent/
https://cloud.google.com/logging/docs/agent/logging/installation#before_you_begin
NEW QUESTION # 65
Case Study: 11 - TerramEarth
Company overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries.
Their mission is to build products that make their customers more productive.
Solution concept
There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth.
Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base.
Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.
Business requirements
- Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
- Decrease cloud operational costs and adapt to seasonality.
- Increase speed and reliability of development workflow.
- Allow remote developers to be productive without compromising code or data security.
- Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements
- Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
- Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
- Allow developers to run experiments without compromising security and governance requirements.
- Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
- Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
- Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in- class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
You have broken down a legacy monolithic application into a few containerized RESTful microservices.
You want to run those microservices on Cloud Run.
You also want to make sure the services are highly available with low latency to your customers.
What should you do?
- A. Deploy Cloud Run services to multiple regions. In Cloud DNS, create a latency-based DNS name that points to the services.
- B. Deploy Cloud Run services to multiple availability zones. Create Cloud Endpoints that point to the services. Create a global HTTP(S) Load Balancing instance and attach the Cloud Endpoints to its backend.
- C. Deploy Cloud Run services to multiple availability zones. Create a TCP/IP global load balancer.
Add the Cloud Run Endpoints to its backend service. - D. Deploy Cloud Run services to multiple regions. Create serverless network endpoint groups pointing to the services. Add the serverless NEGs to a backend service that is used by a global HTTP(S) Load Balancing instance.
Answer: D
Explanation:
Cloud Run is a regional service.
To serve global users you need to configure a Global HTTP LB and NEG as the backend.
Cloud Run services are deployed into individual regions and to route your users to different regions of your service, you need to configure external HTTP(S) Load Balancing.
https://cloud.google.com/run/docs/multiple-regions
A network endpoint group (NEG) specifies a group of backend endpoints for a load balancer.
A serverless NEG is a backend that points to a Cloud Run, App Engine, or Cloud Functions service.
https://cloud.google.com/load-balancing/docs/negs/serverless-neg-concepts
NEW QUESTION # 66
For this question, refer to the JencoMart case study
A few days after JencoMart migrates the user credentials database to Google Cloud Platform and shuts down the old server, the new database server stops responding to SSH connections. It is still serving database requests to the application servers correctly. What three steps should you take to diagnose the problem? Choose 3 answers
- A. Take a snapshot of the disk and connect to a new machine to investigate.
- B. Delete the virtual machine (VM) and disks and create a new one.
- C. Delete the instance, attach the disk to a new VM, and investigate.
- D. Check inbound firewall rules for the network the machine is connected to.
- E. Connect the machine to another network with very simple firewall rules and investigate.
- F. Print the Serial Console output for the instance for troubleshooting, activate the interactive console, and investigate.
Answer: A,D,F
Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-ssh D: Handling "Unable to connect on port 22" error message Possible causes include:
There is no firewall rule allowing SSH access on the port. SSH access on port 22 is enabled on all Compute Engine instances by default. If you have disabled access, SSH from the Browser will not work. If you run sshd on a port other than 22, you need to enable the access to that port with a custom firewall rule.
The firewall rule allowing SSH access is enabled, but is not configured to allow connections from GCP Console services. Source IP addresses for browser-based SSH sessions are dynamically allocated by GCP Console and can vary from session to session.
References:
https://cloud.google.com/compute/docs/ssh-in-browser
https://cloud.google.com/compute/docs/ssh-in-browser
Topic 4, Dress4Win
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 # 67
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements. Which combination of Google technologies will meet all of their requirements?
- A. Container Engine, Cloud Pub/Sub, and Cloud SQL
- B. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
- C. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
- D. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
- E. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
Answer: E
Explanation:
Explanation
A real time requires Stream / Messaging so Pub/Sub, Analytics by Big Query Ingest millions of streaming events per second from anywhere in the world with Cloud Pub/Sub, powered by Google's unique, high-speed private network. Process the streams with Cloud Dataflow to ensure reliable, exactly-once, low-latency data transformation. Stream the transformed data into BigQuery, the cloud-native data warehousing service, for immediate analysis via SQL or popular visualization tools.
From scenario: They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics.
Requirements for Game Analytics Platform
* Dynamically scale up or down based on game activity
* Process incoming data on the fly directly from the game servers
* Process data that arrives late because of slow mobile networks
* Allow SQL queries to access at least 10 TB of historical data
* Process files that are regularly uploaded by users' mobile devices
* Use only fully managed services
References: https://cloud.google.com/solutions/big-data/stream-analytics/
NEW QUESTION # 68
TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. Next
year they want to use the data to train machine learning models. They want to store this data in the cloud
while reducing costs.
What should they do?
- A. Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud
Storage (GCS) Nearline bucket - B. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it
in Cloud Bigtable - C. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it
in Google BigQuery - D. Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline
bucket
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Storage is the best choice for data that you plan to access at most once a year, due to its slightly lower
availability, 90-day minimum storage duration, costs for data access, and higher per-operation costs. For
example:
Cold Data Storage - Infrequently accessed data, such as data stored for legal or regulatory reasons, can
be stored at low cost as Coldline Storage, and be available when you need it.
Disaster recovery - In the event of a disaster recovery event, recovery time is key. Cloud Storage provides
low latency access to data stored as Coldline Storage.
References: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION # 69
Your customer wants to capture multiple GBs of aggregate real-time key performance indicators (KPIs) from their game servers running on Google Cloud Platform and monitor the KPIs with low latency. How should they capture the KPIs?
- A. Schedule BigQuery load jobs to ingest analytics files uploaded to Cloud Storage every ten minutes, and visualize the results in Google Data Studio.
- B. Output custom metrics to Stackdriver from the game servers, and create a Dashboard in Stackdriver Monitoring Console to view them.
- C. Insert the KPIs into Cloud Datastore entities, and run ad hoc analysis and visualizations of them in Cloud Datalab.
- D. Store time-series data from the game servers in Google Bigtable, and view it using Google Data Studio.
Answer: D
Explanation:
Explanation
https://cloud.google.com/monitoring/api/v3/metrics-details#metric-kinds
NEW QUESTION # 70
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The Google Certified Professional - Cloud Architect (GCP) certification is intended for individuals who have experience working with cloud architecture and Google Cloud Platform, including cloud architects, solution architects, infrastructure architects, and cloud developers. It is also suitable for professionals who work with cloud technologies and want to enhance their knowledge and skills in designing and managing cloud solutions.
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