[Apr 19, 2024] Dumps Collection Professional-Cloud-Architect Test Engine Dumps Training With 278 Questions
Google Professional-Cloud-Architect Dumps - 100% Cover Real Exam Questions
Google Professional-Cloud-Architect certification is a valuable credential for IT professionals and cloud architects who want to demonstrate their expertise in designing, developing, and managing cloud solutions using GCP. Google Certified Professional - Cloud Architect (GCP) certification exam covers a range of topics and is designed to evaluate the candidate's ability to design and deploy scalable, reliable, and cost-effective cloud solutions that meet the needs of their organization.
NEW QUESTION # 133
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: D
NEW QUESTION # 134
You need to deploy a stateful workload on Google Cloud. The workload can scale horizontally, but each instance needs to read and write to the same POSIX filesystem. At high load, the stateful workload needs to support up to 100 MB/s of writes. What should you do?
- A. Create a Cloud Filestore instance and mount it in each instance.
- B. Use a regional persistent disk for each instance.
- C. Use a persistent disk for each instance.
- D. Create a Cloud Storage bucket and mount it in each instance using gcsfuse.
Answer: A
Explanation:
https://cloud.google.com/storage/docs/gcs-fuse#notes
Cloud Filestore: Cloud Filestore is a scalable and highly available shared file service fully managed by Google. Cloud Filestore provides persistent storage ideal for shared workloads. It is best suited for enterprise applications requiring persistent, durable, shared storage which is accessed by NFS or requires a POSIX compliant file system.
NEW QUESTION # 135
An application development team has come to you for advice.They are planning to write and deploy an HTTP(S) API using Go 1.12. The API will have a very unpredictable workload and must remain reliable during peaks in traffic. They want to minimize operational overhead for this application. What approach should you recommend?
- A. Use a Managed Instance Group when deploying to Compute Engine
- B. Develop the application for App Engine standard environment
- C. Develop the application for App Engine Flexible environment using a custom runtime
- D. Develop an application with containers, and deploy to Google Kubernetes Engine (GKE)
Answer: B
Explanation:
https://cloud.google.com/appengine/docs/the-appengine-environments
NEW QUESTION # 136
To reduce costs, the Director of Engineering has required all developers to move their development infrastructure resources from on-premises virtual machines (VMs) to Google Cloud Platform. These resources go through multiple start/stop events during the day and require state to persist. You have been asked to design the process of running a development environment in Google Cloud while providing cost visibility to the finance department. Which two steps should you take? Choose 2 answers
- A. Use the -auto-delete flag on all persistent disks and terminate the VM.
- B. Use Google BigQuery billing export and labels to associate cost to groups.
- C. Store all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM.
- D. Apply VM CPU utilization label and include it in the BigQuery billing export.
- E. Store all state into local SSD, snapshot the persistent disks, and terminate the VM.
- F. Use the --no-auto-delete flag on all persistent disks and stop the VM.
Answer: B,F
Explanation:
Explanation
https://cloud.google.com/billing/docs/how-to/export-data-bigquery
NEW QUESTION # 137
For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games. Considering the business and technical requirements, what should you do?
- A. Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.
- B. Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.
- C. Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.
- D. Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.
Answer: D
NEW QUESTION # 138
Dress4Win has end-to-end tests covering 100% of their endpoints.
They want to ensure that the move to the cloud does not introduce any new bugs.
Which additional testing methods should the developers employ to prevent an outage?
- A. They should add additional unit tests and production scale load tests on their cloud staging environment.
- B. They should add canary tests so developers can measure how much of an impact the new release causes to latency.
- C. They should run the end-to-end tests in the cloud staging environment to determine if the code is working as intended.
- D. They should enable Google Stackdriver Debugger on the application code to show errors in the code.
Answer: A
NEW QUESTION # 139
Your company pushes batches of sensitive transaction data from its application server VMs to Cloud Pub/Sub for processing and storage. What is the Google-recommended way for your application to authenticate to the required Google Cloud services?
- A. Ensure that VM service accounts are granted the appropriate Cloud Pub/Sub IAM roles.
- B. Generate an OAuth2 access token for accessing Cloud Pub/Sub, encrypt it, and store it in Cloud Storage for access from each VM.
- C. Create a gateway to Cloud Pub/Sub using a Cloud Function, and grant the Cloud Function service account the appropriate Cloud Pub/Sub IAM roles.
- D. Ensure that VM service accounts do not have access to Cloud Pub/Sub, and use VM access scopes to grant the appropriate Cloud Pub/Sub IAM roles.
Answer: A
NEW QUESTION # 140
Your organization has decided to restrict the use of external IP addresses on instances to only approved instances. You want to enforce this requirement across all of your Virtual Private Clouds (VPCs). What should you do?
- A. Set an Organization Policy with a constraint on constraints/compute.vmExternalIpAccess. List the approved instances in the allowedValues list.
- B. Create a new VPC in custom mode. Create a new subnet for the approved instances, and set a default route to the internet gateway on this new subnet.
- C. Implement a Cloud NAT solution to remove the need for external IP addresses entirely.
- D. Remove the default route on all VPCs. Move all approved instances into a new subnet that has a default route to an internet gateway.
Answer: A
Explanation:
Reference: https://cloud.google.com/compute/docs/ip-addresses/reserve-static-external-ip-address
NEW QUESTION # 141
For this question, refer to the Dress4Win case study.
As part of Dress4Win's plans to migrate to the cloud, they want to be able to set up a managed logging and monitoring system so they can handle spikes in their traffic load. They want to ensure that:
* The infrastructure can be notified when it needs to scale up and down to handle the ebb and flow of usage throughout the day
* Their administrators are notified automatically when their application reports errors.
* They can filter their aggregated logs down in order to debug one piece of the application across many hosts Which Google StackDriver features should they use?
- A. Monitoring, Logging, Alerts, Error Reporting
- B. Monitoring, Trace, Debug, Logging
- C. Monitoring, Logging, Debug, Error Report
- D. Logging, Alerts, Insights, Debug
Answer: C
Explanation:
Topic 6, Dress4Win
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. The 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 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 # 142
Your company acquired a healthcare startup and must retain its customers' medical information for up to 4 more years, depending on when it was created. Your corporate policy is to securely retain this data, and then delete it as soon as regulations allow.
Which approach should you take?
- A. Store the data in Google Drive and manually delete records as they expire.
- B. Anonymize the data using the Cloud Data Loss Prevention API and store it indefinitely.
- C. Store the data in Cloud Storage and use lifecycle management to delete files when they expire.
- D. Store the data in Cloud Storage and run a nightly batch script that deletes all expired data.
Answer: C
NEW QUESTION # 143
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 GKE CPU Utilization and Memory Utilization as service level indicators.
- B. Create CPU Utilization and Request Latency as service level indicators.
- C. Create Request Latency and Error Rate as service level indicators.
- D. Create Server Uptime and Error Rate as service level indicators.
Answer: C
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 # 144
Your company and one of its partners each nave a Google Cloud protect in separate organizations. Your company s protect (prj-a) runs in Virtual Private Cloud (vpc-a). The partner's project (prj-b) runs in vpc-b.
There are two instances running on vpc-a and one instance running on vpc-b Subnets denned in both VPCs are not overlapping. You need to ensure that all instances communicate with each other via internal IPs minimizing latency and maximizing throughput. What should you do?
- A. Configure IAP TCP forwarding on the instance in vpc b and then launch the following gcloud command from one of the instance in vpc-gcloud:
1. Create an additional instance in vpc-a
2. Create an additional instance n vpc-b
3. Instal OpenVPN in newly created instances
4. Configure a VPN tunnel between vpc-a and vpc-b with the help of OpenVPN - B. Set up a VPN between vpc-a and vpc-b using Cloud VPN
- C. Set up a network peering between vpc-a and vpc-b
Answer: A
NEW QUESTION # 145
You need to develop procedures to verify resilience of disaster recovery for remote recovery using GCP. Your production environment is hosted on-premises. You need to establish a secure, redundant connection between your on premises network and the GCP network.
What should you do?
- A. Verify that the Transfer Appliance can replicate files to GCP. Verify that Cloud VPN can establish a secure connection between your networks if the Transfer Appliance fails.
- B. Verify that Dedicated Interconnect can replicate files to GCP. Verify that direct peering can establish a secure connection between your networks if Dedicated Interconnect fails.
- C. Verify that the Transfer Appliance can replicate files to GCP. Verify that direct peering can establish a secure connection between your networks if the Transfer Appliance fails.
- D. Verify that Dedicated Interconnect can replicate files to GCP. Verify that Cloud VPN can establish a secure connection between your networks if Dedicated Interconnect fails.
Answer: D
Explanation:
Reference:
https://cloud.google.com/interconnect/docs/how-to/direct-peering
NEW QUESTION # 146
Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field.
How can you accomplish this goal?
- A. Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically
- B. Have you engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically
- C. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically
- D. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
Answer: A
Explanation:
Explanation/Reference:
References: https://cloud.google.com/customers/ocado/
TerramEarth, B
Testlet 1
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.
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 and Windows-based systems that reside in a single
U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, 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.
* Support the dealer network with more data on how their customers use their equipment to 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.
Technical Requirements
* Expand beyond a single datacenter to decrease latency to the American Midwest and east coast.
* Create a backup strategy.
* Increase security of data transfer from equipment to the datacenter.
* Improve data in the data warehouse.
* Use customer and equipment data to anticipate customer needs.
Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
* Windows Server 2008 R2
- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair.
Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
* Off the shelf application. License tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
* A single PostgreSQL server
- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in our manufacturing process, with our ability to build better vehicles for lower 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. My goals are to build our skills while addressing immediate market needs through incremental innovations.
NEW QUESTION # 147
You are implementing a single Cloud SQL MySQL second-generation database that contains business-critical transaction data. You want to ensure that the minimum amount of data is lost in case of catastrophic failure.
Which two features should you implement? (Choose two.)
- A. Semisynchronous replication
- B. Sharding
- C. Binary logging
- D. Automated backups
- E. Read replicas
Answer: C,D
Explanation:
Explanation
Backups help you restore lost data to your Cloud SQL instance. Additionally, if an instance is having a problem, you can restore it to a previous state by using the backup to overwrite it. Enable automated backups for any instance that contains necessary data. Backups protect your data from loss or damage.
Enabling automated backups, along with binary logging, is also required for some operations, such as clone and replica creation.
Reference: https://cloud.google.com/sql/docs/mysql/backup-recovery/backups
NEW QUESTION # 148
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. TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry dat
- A. 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?
- B. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
- C. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
- D. Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
- E. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
Answer: C
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 # 149
For this question, refer to the Dress4Win case study. Which of the compute services should be migrated as -is and would still be an optimized architecture for performance in the cloud?
- A. Web applications deployed using App Engine standard environment
- B. Jenkins, monitoring, bastion hosts, security scanners services deployed on custom machine types
- C. Hadoop/Spark deployed using Cloud Dataproc Regional in High Availability mode
- D. RabbitMQ deployed using an unmanaged instance group
Answer: C
NEW QUESTION # 150
For this question, refer to the JencoMart case study.
The migration of JencoMart's application to Google Cloud Platform (GCP) is progressing too slowly. The infrastructure is shown in the diagram. You want to maximize throughput. What are three potential bottlenecks? (Choose 3 answers.)
- A. A copy command that is not suited to operate over long distances
- B. A separate storage layer outside the VMs, which is not suited for this task
- C. Fewer virtual machines (VMs) in GCP than on-premises machines
- D. A single VPN tunnel, which limits throughput
- E. A tier of Google Cloud Storage that is not suited for this task
- F. Complicated internet connectivity between the on-premises infrastructure and GCP
Answer: C,D,F
NEW QUESTION # 151
As part of their new application experience, Dress4Wm allows customers to upload images of themselves.
The customer has exclusive control over who may view these images.
Customers should be able to upload images with minimal latency and also be shown their images quickly
on the main application page when they log in.
Which configuration should Dress4Win use?
- A. Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata
that maps each customer's ID and their image files. - B. Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in
Cloud Storage that contains the customer's unique ID. - C. Use a distributed file system to store customers' images. As storage needs increase, add more
persistent disks and/or nodes. Assign each customer a unique ID, which sets each file's owner
attribute, ensuring privacy of images. - D. Use a distributed file system to store customers' images. As storage needs increase, add more
persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps
each customer's ID to their image files.
Answer: A
NEW QUESTION # 152
A development team at your company has created a dockerized HTTPS web application. You need to deploy the application on Google Kubernetes Engine (GKE) and make sure that the application scales automatically.
How should you deploy to GKE?
- A. Use the Horizontal Pod Autoscaler and enable cluster autoscaling on the Kubernetes cluster. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.
- B. Use the Horizontal Pod Autoscaler and enable cluster autoscaling. Use an Ingress resource to load-balance the HTTPS traffic.
- C. Enable autoscaling on the Compute Engine instance group. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.
- D. Enable autoscaling on the Compute Engine instance group. Use an Ingress resource to load balance the HTTPS traffic.
Answer: A
Explanation:
Explanation/Reference:
Reference https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-autoscaler
NEW QUESTION # 153
For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)
- A. Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.
- B. Begin packaging their game backend artifacts in container images and running them on Kubernetes Engine to improve the availability to scale up or down based on game activity.
- C. Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.
- D. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.
- E. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.
Answer: B,E
NEW QUESTION # 154
You are using Cloud SQL as the database backend for a large CRM deployment. You want to scale as usage increases and ensure that you don't run out of storage, maintain 75% CPU usage cores, and keep replication lag below 60 seconds. What are the correct steps to meet your requirements?
- A. 1. Enable automatic storage increase for the instance.
2. Create a Stackdriver alert when CPU usage exceeds 75%, and change the instance type to reduce CPU usage.
3. Create a Stackdriver alert for replication lag, and shard the database to reduce replication time. - B. 1. Create a Stackdriver alert when storage exceeds 75%, and increase the available storage on the instance to create more space.
2. Deploy memcached to reduce CPU load.
3. Change the instance type to a 32-core machine type to reduce replication lag. - C. 1. Enable automatic storage increase for the instance.
2. Change the instance type to a 32-core machine type to keep CPU usage below 75%.
3. Create a Stackdriver alert for replication lag, and deploy memcache to reduce load on the master. - D. 1. Create a Stackdriver alert when storage exceeds 75%, and increase the available storage on the instance to create more space.
2. Deploy memcached to reduce CPU load.
3. Create a Stackdriver alert for replication lag, and change the instance type to a 32-core machine type to reduce replication lag.
Answer: A
NEW QUESTION # 155
......
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