Start your Professional-Cloud-Architect Exam Questions Preparation with Updated 378 Questions
A Fully Updated 2026 Professional-Cloud-Architect Exam Dumps - PDF Questions and Testing Engine
Managing & Provisioning Solution Infrastructures
- Configure network topologies: the examinees should have the ability to extend to hybrid and on-premises networking and multi-Cloud environment that may entail GCP to GCP communication. It also requires their understanding of data protection and security;
- Configure individual storage systems: the areas of focus should include data storage allocation; access management and security; data processing and compute provisioning; data lifecycle management and data retention; network configuration for the data latency and transfer;
- Configure compute systems: you should understand system provisioning; compute volatility configuration; container orchestration using Kubernetes; technology configuration for infrastructure provisioning; network configuration for the compute nodes.
NEW QUESTION # 191
You are helping the QA team to roll out a new load-testing tool to test the scalability of your primary cloud services that run on Google Compute Engine with Cloud Bigtable. Which three requirements should they include? Choose 3 answers
- A. Ensure all third-party systems your services use are capable of handling high load.
- B. Instrument the load-testing tool and the target services with detailed logging and metrics collection.
- C. Schedule the load-testing tool to regularly run against the production environment.
- D. Ensure that the load tests validate the performance of Cloud Bigtable.
- E. Instrument the production services to record every transaction for replay by the load-testing tool.
- F. Create a separate Google Cloud project to use for the load-testing environment.
Answer: C,D,E
NEW QUESTION # 192
You are working at a financial institution that stores mortgage loan approval documents on Cloud Storage. Any change to these approval documents must be uploaded as a separate approval file, so you want to ensure that these documents cannot be deleted or overwritten for the next 5 years. What should you do?
- A. Create the bucket with fine-grained access control, and grant a service account the role of Object Writer. Use the service account to upload new files.
- B. Create the bucket with uniform bucket-level access, and grant a service account the role of Object Writer. Use the service account to upload new files.
- C. Create a retention policy on the bucket for the duration of 5 years. Create a lock on the retention policy.
- D. Use a customer-managed key for the encryption of the bucket. Rotate the key after 5 years.
Answer: C
NEW QUESTION # 193
You are using a single Cloud SQL instance to serve your application from a specific zone. You want to introduce high availability. What should you do?
- A. Create a failover replica instance in the same region, but in a different zone
- B. Create a failover replica instance in a different region
- C. Create a read replica instance in the same region, but in a different zone
- D. Create a read replica instance in a different region
Answer: B
NEW QUESTION # 194
Your customer support tool logs all email and chat conversations to Cloud Bigtable for retention and analysis. What is the recommended approach for sanitizing this data of personally identifiable information or payment card information before initial storage?
- A. Encrypt all data using elliptic curve cryptography
- B. Use regular expressions to find and redact phone numbers, email addresses, and credit card numbers
- C. Hash all data using SHA256
- D. De-identify the data with the Cloud Data Loss Prevention API
Answer: D
Explanation:
https://cloud.google.com/solutions/pci-dss-compliance-
ingcp#using_data_loss_prevention_api_to_sanitize_data
NEW QUESTION # 195
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 Bigtable
- D. Google Cloud SQL
Answer: C
Explanation:
It is time-series data, So Big Table.
https://cloud.google.com/bigtable/docs/schema-design-time-series
Google Cloud Bigtable is a scalable, fully-managed NoSQL wide-column database that is suitable for both real-time access and analytics workloads.
Good for:
Low-latency read/write access
High-throughput analytics
Native time series support
Common workloads:
IoT, finance, adtech
Personalization, recommendations
Monitoring
Geospatial datasets
Graphs
References: https://cloud.google.com/storage-options/
NEW QUESTION # 196
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. Create an HTTP request to the built-in python module to send the index configuration file to your application
- B. Point gcloud datastore create-indexesto your configuration file
- C. Upload the configuration file to App Engine's default Cloud Storage bucket, and have App Engine detect the new indexes
- D. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file
Answer: B
NEW QUESTION # 197
You are launching a data fabric service on Google Cloud in ten weeks and need to transfer 2 petabytes of historical data from your on-premises data center to Google Cloud. You want to securely transfer the data with low latency. What should you do?
- A. Export the data to portable disks and send the disks to Google Cloud for data import.
- B. Create a 1 Gbps VPN connection between your data center and Google Cloud. Use Storage Transfer Service to transfer the data.
- C. Request Transfer Appliances from Google Cloud. Transfer the data using the appliances.
- D. Create a 1 Gbps connection using direct interconnect. Use the Storage Transfer Service for on- premises data to transfer the data.
Answer: C
Explanation:
Transfer Appliance is designed for secure, large-scale offline migrations when you need to move petabytes of data within a fixed timeline. It avoids network bandwidth constraints and provides a controlled, secure transfer process suitable for 2 PB within ten weeks.
NEW QUESTION # 198
Case Study: 1 - Mountkirk Games Case Study
Company Overview
Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms.
Company Background
Mountkirk Games builds all of their games with some server-side integration and has historically used cloud providers to lease physical servers. A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools.
Mountkirk's current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Technical Requirements
Requirements for Game Backend Platform
1. Dynamically scale up or down based on game activity.
2. Connect to a managed NoSQL database service.
3. Run customized Linx distro.
Requirements for Game Analytics Platform
1. Dynamically scale up or down based on game activity.
2. Process incoming data on the fly directly from the game servers.
3. Process data that arrives late because of slow mobile networks.
4. Allow SQL queries to access at least 10 TB of historical data.
5. Process files that are regularly uploaded by users' mobile devices.
6. Use only fully managed services
CEO Statement
Our last successful game did not scale well with our previous cloud provider, resuming in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the gams to target users.
CTO Statement
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
CFO Statement
We are not capturing enough user demographic data usage metrics, and other KPIs. As a result, we do not engage the right users. We are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.
For this question, refer to the Mountkirk Games case study Mountkirk Games needs to create a repeatable and configurable mechanism for deploying isolated application environments.
Developers and testers can access each other's environments and resources, but they cannot access staging or production resources. The staging environment needs access to some services from production.
What should you do to isolate development environments from staging and production?
- A. Create a project for development and test and another for staging and production.
- B. Create one subnetwork for development and another for staging and production.
- C. Create a network for development and test and another for staging and production.
- D. Create one project for development, a second for staging and a third for production.
Answer: D
Explanation:
Google recommends using shared VPC and creating separate projects for different environments. Staging and Prod EnV should be connected to one VCP in Shared VPC thus they are in separate projects but that they can communicate with each other securely and efficiently using internal IPs from that network.
https://cloud.google.com/appengine/docs/standard/go/creating-separate-dev-environments
NEW QUESTION # 199
Your customer wants to do resilience testing of their authentication layer. This consists of a regional managed instance group serving a public REST API that reads from and writes to a Cloud SQL instance.
What should you do?
- A. Schedule a disaster simulation exercise during which you can shut off all VMs in a zone to see how your application behaves.
- B. Configure a read replica for your Cloud SQL instance in a different zone than the master, and then manually trigger a failover while monitoring KPIs for our REST API.
- C. Engage with a security company to run web scrapers that look your users' authentication data om malicious websites and notify you if any if found.
- D. Deploy intrusion detection software to your virtual machines to detect and log unauthorized access.
Answer: D
NEW QUESTION # 200
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 # 201
You have been engaged by your client to lead the migration of their application infrastructure to GCP. One of their current problems is that the on-premises high performance SAN is requiring frequent and expensive upgrades to keep up with the variety of workloads that are identified as follows: 20TB of log archives retained for legal reasons; 500 GB of VM boot/data volumes and templates; 500 GB of image thumbnails; 200 GB of customer session state data that allows customers to restart sessions even if off-line for several days. Which of the following best reflects your recommendations for a cost-effective storage allocation?
- A. Local SSD for customer session state data. Lifecycle-managed Cloud Storage for log archives, thumbnails, and VM boot/data volumes.
- B. Memcache backed by Cloud SQL for customer session state data. Assorted local SSD-backed instances for VM boot/data volumes. Cloud Storage for log archives and thumbnails.
- C. Memcache backed by Cloud Datastore for the customer session state data. Lifecycle- managed Cloud Storage for log archives, thumbnails, and VM boot/data volumes.
- D. Memcache backed by Persistent Disk SSD storage for customer session state data. Assorted local SSD-backed instances for VM boot/data volumes. Cloud Storage for log archives and thumbnails.
Answer: C
Explanation:
Memcache backed by Cloud Datastore
https://cloud.google.com/appengine/docs/standard/python/memcache
Compute Engine image can be stored in Cloud Storage
https://cloud.google.com/solutions/image-management-best-practices
After the complete sequence of bytes from the disk are written to the file, the file is archived using the tar format and then compressed using the GZIP format. You can then upload the resulting
*.tar.gz file to Cloud Storage and register it as an image in Compute Engine.
NEW QUESTION # 202
For this question, refer to the TerramEarth case study
Your development team has created a structured API to retrieve vehicle dat a. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?
- A. Create secondary credentials for each dealer that can be given to the trusted third party.
- B. Build SAML 2.0 SSO compatibility into your authentication system.
- C. Build or leverage an OAuth-compatible access control system.
- D. Restrict data access based on the source IP address of the partner systems.
Answer: C
Explanation:
Delegate application authorization with OAuth2
Cloud Platform APIs support OAuth 2.0, and scopes provide granular authorization over the methods that are supported. Cloud Platform supports both service-account and user-account OAuth, also called three-legged OAuth.
References: https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
Reference:
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2
NEW QUESTION # 203
Case Study: 6 - TerramEarth
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 the 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.
For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?
- A. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.
- B. Create a BigQuery table for the European data, and set the table retention period to 36 months.
For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months. - C. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
- D. Create a BigQuery table for the European data, and set the table retention period to 36 months.
For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
Answer: C
NEW QUESTION # 204
Your customer support tool logs all email and chat conversations to Cloud Bigtable for retention and analysis.
What is the recommended approach for sanitizing this data of personally identifiable information or payment card information before initial storage?
- A. Encrypt all data using elliptic curve cryptography
- B. Use regular expressions to find and redact phone numbers, email addresses, and credit card numbers
- C. Hash all data using SHA256
- D. De-identify the data with the Cloud Data Loss Prevention API
Answer: D
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/pci-dss-compliance-in- gcp#using_data_loss_prevention_api_to_sanitize_data
NEW QUESTION # 205
You want to make a copy of a production Linux virtual machine in the US-Central region. You want to manage and replace the copy easily if there are changes on the production virtual machine. You will deploy the copy as a new instance in a different project in the US-East region.
What steps must you take?
- A. Create a snapshot of the root disk and select the snapshot as the root disk when you create a new virtual machine instance in the US-East region.
- B. Create a snapshot of the root disk, create an image file in Google Cloud Storage from the snapshot, and create a new virtual machine instance in the US-East region using the image file the root disk.
- C. Create an image file from the root disk with Linux dd command, create a new virtual machine instance in the US-East region
- D. Use the Linux dd and netcat commands to copy and stream the root disk contents to a new virtual machine instance in the US-East region.
Answer: B
NEW QUESTION # 206
Case Study: 7 - Mountkirk Games
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Business Requirements
Increase to a global footprint.
Improve uptime - downtime is loss of players.
Increase efficiency of the cloud resources we use.
Reduce latency to all customers.
Technical Requirements
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity.
Connect to a transactional database service to manage user profiles and game state.
Store game activity in a timeseries database service for future analysis.
As the system scales, ensure that data is not lost due to processing backlogs.
Run hardened Linux distro.
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 queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users.
Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
For this question, refer to the Mountkirk Games case study. You are in charge of the new Game Backend Platform architecture. The game communicates with the backend over a REST API.
You want to follow Google-recommended practices. How should you design the backend?
- A. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.
- B. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.
- C. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.
- D. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.
Answer: B
NEW QUESTION # 207
Your company is planning to upload several important files to Cloud Storage. After the upload is completed, they want to verify that the upload content is identical to what they have on- premises. You want to minimize the cost and effort of performing this check. What should you do?
- A. 1)Use gsutil -m to upload all the files to Cloud Storage.
2)Use gsutil hash -c FILE_NAME to generate CRC32C hashes of all on-premises files
3)Use gsutil ls -L gs://[YOUR_BUCKET_NAME] to collect CRC32C hashes of the uploaded files
4)Compare the hashes - B. 1) Use gsutil -m to upload all the files to Cloud Storage.
2) Develop a custom Java application that computes CRC32C hashes
3) Use gsutil ls -L gs://[YOUR_BUCKET_NAME] to collect CRC32C hashes of the uploaded files
4)Compare the hashes - C. 1) Use Linux shasum to compute a digest of files you want to upload
2) Use gsutil -m to upload all the files to the Cloud Storage
3) Use gsutil cp to download the uploaded files
4) Use Linux shasum to compute a digest of the downloaded files 5.Compre the hashes - D. 1) Use gsutil -m to upload all the files to Cloud Storage.
2) Use gsutil cp to download the uploaded files
3) Use Linux diff to compare the content of the files
Answer: A
Explanation:
https://cloud.google.com/storage/docs/gsutil/commands/hash
NEW QUESTION # 208
You have developed an application using Cloud ML Engine that recognizes famous paintings from uploaded images. You want to test the application and allow specific people to upload images for the next 24 hours. Not all users have a Google Account. How should you have users upload images?
- A. Have users upload the images to Cloud Storage using a signed URL that expires after 24 hours.
- B. Create an App Engine web application where users can upload images. Configure App Engine to disable the application after 24 hours. Authenticate users via Cloud Identity.
- C. Have users upload the images to Cloud Storage. Protect the bucket with a password that expires after 24 hours.
- D. Create an App Engine web application where users can upload images for the next 24 hours.
Authenticate users via Cloud Identity.
Answer: A
Explanation:
When should you use a signed URL? In some scenarios, you might not want to require your users to have a Google account in order to access Cloud Storage" "Signed URLs contain authentication information in their query string, allowing users without credentials to perform specific actions on a resource".
https://cloud.google.com/storage/docs/access-control/signed-urls
NEW QUESTION # 209
A lead software engineer tells you that his new application design uses websockets and HTTP sessions that are not distributed across the web servers. You want to help him ensure his application will run property on Google Cloud Platform. What should you do?
- A. Meet with the cloud operations team and the engineer to discuss load balancer options.
- B. Review the encryption requirements for websocket connections with the security team.
- C. Help the engineer to convert his websocket code to use HTTP streaming.
- D. Help the engineer redesign the application to use a distributed user session service that does not rely on websockets and HTTP sessions.
Answer: A
Explanation:
Google Cloud Platform (GCP) HTTP(S) load balancing provides global load balancing for HTTP(S) requests destined for your instances.
The HTTP(S) load balancer has native support for the WebSocket protocol.
Incorrect Answers:
A: HTTP server push, also known as HTTP streaming, is a client-server communication pattern that sends information from an HTTP server to a client asynchronously, without a client request. A server push architecture is especially effective for highly interactive web or mobile applications, where one or more clients need to receive continuous information from the server.
References:
https://cloud.google.com/compute/docs/load-balancing/http/
NEW QUESTION # 210
Case Study: 1 - Mountkirk Games Case Study
Company Overview
Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms.
Company Background
Mountkirk Games builds all of their games with some server-side integration and has historically used cloud providers to lease physical servers. A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools.
Mountkirk's current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Technical Requirements
Requirements for Game Backend Platform
1. Dynamically scale up or down based on game activity.
2. Connect to a managed NoSQL database service.
3. Run customized Linx distro.
Requirements for Game Analytics Platform
1. Dynamically scale up or down based on game activity.
2. Process incoming data on the fly directly from the game servers.
3. Process data that arrives late because of slow mobile networks.
4. Allow SQL queries to access at least 10 TB of historical data.
5. Process files that are regularly uploaded by users' mobile devices.
6. Use only fully managed services
CEO Statement
Our last successful game did not scale well with our previous cloud provider, resuming in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the gams to target users.
CTO Statement
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
CFO Statement
We are not capturing enough user demographic data usage metrics, and other KPIs. As a result, we do not engage the right users. We are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.
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. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
- B. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
- C. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
- D. Container Engine, Cloud Pub/Sub, and Cloud SQL
- E. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
Answer: E
Explanation:
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
1. Dynamically scale up or down based on game activity
2. Process incoming data on the fly directly from the game servers
3. Process data that arrives late because of slow mobile networks
4. Allow SQL queries to access at least 10 TB of historical data
5. Process files that are regularly uploaded by users' mobile devices
6. Use only fully managed services
References: https://cloud.google.com/solutions/big-data/stream-analytics/
NEW QUESTION # 211
For this question, refer to the TerramEarth case study.
TerramEarth's CTO wants to use the raw data from connected vehicles to help identify approximately when a vehicle in the development team to focus their failure. You want to allow analysts to centrally query the vehicle data. Which architecture should you recommend?
- A. Option B
- B. Option A
- C. Option C
- D. Option D
Answer: B
Explanation:
https://cloud.google.com/solutions/iot/
https://cloud.google.com/solutions/designing-connected-vehicle-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform#data_ingestion
http://www.eweek.com/big-data-and-analytics/google-touts-value-of-cloud-iot-core-for-analyzing- connected-car-data
https://cloud.google.com/solutions/iot/
NEW QUESTION # 212
Case Study: 5 - 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.
For this question, refer to the Dress4Win case study. Considering the given business requirements, how would you automate the deployment of web and transactional data layers?
- A. Deploy Nginx and Tomcat using Cloud Launcher. Deploy a MySQL server using Cloud Launcher.
Deploy Jenkins to Compute Engine using Cloud Deployment Manager scripts. - B. Deploy Nginx and Tomcat using Cloud Deployment Manager to Compute Engine. Deploy a Cloud SQL server to replace MySQL. Deploy Jenkins using Cloud Deployment Manager.
- C. Migrate Nginx and Tomcat to App Engine. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Launcher.
- D. Migrate Nginx and Tomcat to App Engine. Deploy a Cloud Datastore server to replace the MySQL server in a high-availability configuration. Deploy Jenkins to Compute Engine using Cloud Launcher.
Answer: B
Explanation:
"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."
NEW QUESTION # 213
Your company uses the Firewall Insights feature in the Google Network Intelligence Center. You have several firewall rules applied to Compute Engine instances. You need to evaluate the efficiency of the applied firewall ruleset. When you bring up the Firewall Insights page in the Google Cloud Console, you notice that there are no log rows to display. What should you do to troubleshoot the issue?
- A. Enable Firewall Rules Logging for the firewall rules you want to monitor.
- B. Enable Virtual Private Cloud (VPC) flow logging.
- C. Verify that your user account is assigned the compute.networkAdmin Identity and Access Management (IAM) role.
- D. Install the Google Cloud SDK, and verify that there are no Firewall logs in the command line output.
Answer: A
Explanation:
When you create a firewall rule there is an option for firewall rule logging on/off. It is set to off by default.
To get firewall insights or view the logs for a specific firewall rule you need to enable logging while creating the rule or you can enable it by editing that rule.
https://cloud.google.com/network-intelligence-center/docs/firewall-insights/how-to/using-firewall- insights#enabling-fw-rules-logging
NEW QUESTION # 214
One of your primary business objectives is being able to trust the data stored in your application.
You want to log all changes to the application data. How can you design your logging system to verify authenticity of your logs?
- A. Use a SQL database and limit who can modify the log table.
- B. Write the log concurrently in the cloud and on premises.
- C. Digitally sign each timestamp and log entry and store the signature.
- D. Create a JSON dump of each log entry and store it in Google Cloud Storage.
Answer: D
Explanation:
Write a log entry. If the log does not exist, it is created. You can specify a severity for the log entry, and you can write a structured log entry by specifying --payload-type=json and writing your message as a JSON string:
gcloud logging write LOG STRING
gcloud logging write LOG JSON-STRING --payload-type=json
References: https://cloud.google.com/logging/docs/reference/tools/gcloud-logging
NEW QUESTION # 215
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