Online Professional-Cloud-Architect Test Brain Dump Question and Test Engine
Real Google Professional-Cloud-Architect Exam Dumps with Correct 270 Questions and Answers
Designing for Compliance and Security
- Design for security: the learners need to gain knowledge of penetration testing; identity and access management; resource hierarchy; separation of duties; security control; data security; customer-managed encryption key management with Cloud KMS;
- Design for compliance: this covers one’s knowledge of legislation (health record privacy, data privacy, ownership, and children’s privacy); commercial (sensitive data handling and personally identifiable information); industry certifications (SOC 2); audits (such as logs).
NEW QUESTION # 51
Case Study: 3 - JencoMart Case Study
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company's core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world's leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S.
data centers.
Database
* Oracle Database stores user profiles
* PostgreSQL database stores user credentials
-homed in US West
Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:
* 20 machines in US East Coast, each machine has:
-core CPU
RAID 1)
Storage
* Access to shared 100 TB SAN in each location
* Tape backup every week
Business Requirements
* Optimize for capacity during peak periods and value during off-peak periods
* Guarantee service availably and support
* Reduce on-premises footprint and associated financial and environmental impact.
* Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase
* Expand services into Asia.
Technical Requirements
* Assess key application for cloud suitability.
* Modify application for the cloud.
* Move applications to a new infrastructure.
* Leverage managed services wherever feasible
* Sunset 20% of capacity in existing data centers
* Decrease latency in Asia
CEO Statement
JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large global company, we also have a responsibility to the environment through 'green' initiatives and polices.
CTO Statement
The challenges of operating data centers prevents focus on key technologies critical to our long- term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service customers.
CFO Statement
Since its founding JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long- term success. This model will allow us to respond to increasing customer demand during peak and reduce costs.
For this question, refer to the JencoMart case study.
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 single VPN tunnel, which limits throughput
- B. A tier of Google Cloud Storage that is not suited for this task
- C. Fewer virtual machines (VMs) in GCP than on-premises machines
- D. A copy command that is not suited to operate over long distances
- E. A separate storage layer outside the VMs, which is not suited for this task
- F. Complicated internet connectivity between the on-premises infrastructure and GCP
Answer: A,D,E
NEW QUESTION # 52
The operations team in your company wants to save Cloud VPN log events (or one year You need to configure the cloud infrastructure to save the logs What should you do?
- A. Enable the Compute Engine API and then enable logging on the firewall rules that match the traffic you want to save
- B. Set up a Cloud Logging Dashboard titled Cloud VPN Logs, and then add a chart that queries for the VPN metrics over a one-year time period
- C. Set up a filter in Cloud Logging and a Cloud Storage bucket as an export target for the logs you want to save
- D. Set up a filter in Cloud Logging and a topic in Pub/Sub to publish the logs
Answer: C
NEW QUESTION # 53
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?
- A. Unit tests are no longer required, only end-to-end tests.
- B. Tests should include directly testing the Google Cloud Platform (GCP) infrastructure.
- C. Tests should be applied after the release is in the production environment.
- D. Tests should scale well beyond the prior approaches.
Answer: D
Explanation:
From Scenario:
A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools.
Requirements for Game Analytics Platform include: Dynamically scale up or down based on game activity
NEW QUESTION # 54
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. Store all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM.
- B. Use Google BigQuery billing export and labels to associate cost to groups.
- C. Use the --no-auto-delete flag on all persistent disks and stop the VM.
- D. Store all state into local SSD, snapshot the persistent disks, and terminate the VM.
- E. Use the -auto-delete flag on all persistent disks and terminate the VM.
- F. Apply VM CPU utilization label and include it in the BigQuery billing export.
Answer: B,C
NEW QUESTION # 55
For this question, refer to the Dress4Win case study. To be legally compliant during an audit, Dress4Win must be able to give insights in all administrative actions that modify the configuration or metadata of resources on Google Cloud.
What should you do?
- A. Enable Cloud Identity-Aware Proxy in all projects, and add the group of Administrators as a member.
- B. Use Stackdriver Trace to create a trace list analysis.
- C. Use the Activity page in the GCP Console and Stackdriver Logging to provide the required insight.
- D. Use Stackdriver Monitoring to create a dashboard on the project's activity.
Answer: B
Explanation:
Explanation
https://cloud.google.com/logging/docs/audit/
NEW QUESTION # 56
Your marketing department wants to send out a promotional email campaign. The development team wants to minimize direct operation management. They project a wide range of possible customer responses, from 100 to 500,000 click-throughs per day. The link leads to a simple website that explains the promotion and collects user information and preferences. Which infrastructure should you recommend? (CHOOSE TWO)
- A. Use a managed instance group to serve the website and Google Cloud Bigtable to store user data.
- B. Use a Google Container Engine cluster to serve the website and store data to persistent disk.
- C. Use Google App Engine to serve the website and Google Cloud Datastore to store user data.
- D. Use a single compute Engine virtual machine (VM) to host a web server, backed by Google Cloud SQL.
Answer: A,C
Explanation:
Reference:
References: https://cloud.google.com/storage-options/
NEW QUESTION # 57
For this question, refer to the TerramEarth case study. You are asked to design a new architecture for the ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow Google-recommended practices.
Considering the technical requirements, which components should you use for the ingestion of the data?
- A. Compute Engine with specific SSH keys
- B. Cloud IoT Core with public/private key pairs
- C. Google Kubernetes Engine with an SSL Ingress
- D. Compute Engine with project-wide SSH keys
Answer: B
Explanation:
Explanation
https://cloud.google.com/solutions/iot-overview
NEW QUESTION # 58
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. Put flat files into Google Cloud Storage.
- B. Load data into Google BigQuery.
- C. Stream data into Google Cloud Datastore.
- D. Insert data into Google Cloud SQL.
Answer: B
Explanation:
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/
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/
NEW QUESTION # 59
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. Mountkirk Games wants to migrate from their current analytics and statistics reporting model to one that meets their technical requirements on Google Cloud Platform.
Which two steps should be part of their migration plan? (Choose two.)
- A. Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.
- B. Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.
- C. Load 10 TB of analytics data from a previous game into a Cloud SQL instance, and run test queries against the full dataset to confirm that they complete successfully.
- D. Write a schema migration plan to denormalize data for better performance in BigQuery.
- E. Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.
Answer: D,E
NEW QUESTION # 60
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 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. - 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 (GKE) 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. - D. 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
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.
Answer: C
Explanation:
https://cloud.google.com/compute/docs/instance-templates
NEW QUESTION # 61
For this question, refer to the JencoMart case study.
JencoMart wants to move their User Profiles database to Google Cloud Platform. Which Google Database should they use?
- A. Google BigQuery
- B. Google Cloud Datastore
- C. Google Cloud SQL
- D. Cloud Spanner
Answer: B
Explanation:
Explanation
https://cloud.google.com/datastore/docs/concepts/overview
Common workloads for Google Cloud Datastore:
* User profiles
* Product catalogs
* Game state
References: https://cloud.google.com/storage-options/
https://cloud.google.com/datastore/docs/concepts/overview
NEW QUESTION # 62
Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions.
What should you do?
- A. Configure the quotas for resources in the regions not being used to 0.
- B. Configure an organizational policy which constrains where resources can be deployed.
- C. Configure IAM conditions to limit what resources can be configured.
- D. Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other
Answer: A
Explanation:
regions.
NEW QUESTION # 63
You are creating a solution to remove backup files older than 90 days from your backup Cloud Storage bucket. You want to optimize ongoing Cloud Storage spend. What should you do?
- A. Write a lifecycle management rule in XML and push it to the bucket with gsutil.
- B. Schedule a cron script using gsutil is -lr gs://backups/** to find and remove items older than 90 days.
- C. Write a lifecycle management rule in JSON and push it to the bucket with gsutil.
- D. Schedule a cron script using gsutil ls -1 gs://backups/** to find and remove items older than 90 days and schedule it with cron.
Answer: D
NEW QUESTION # 64
You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customer's 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 FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor
- B. Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics
- C. Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics
- D. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics
Answer: C
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.
Reference: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data
NEW QUESTION # 65
Your company is building a new architecture to support its data-centric business focus. You are responsible for setting up the network. Your company's mobile and web-facing applications will be deployed on-premises, and all data analysis will be conducted in GCP. The plan is to process and load 7 years of archived .csv files totaling
900 TB of data and then continue loading 10 TB of data daily. You currently have an existing 100-MB internet connection.
What actions will meet your company's needs?
- A. Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish a connection with Google using a Dedicated Interconnect or Direct Peering connection and use it to upload files daily.
- B. Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish a Cloud VPN Tunnel to VPC networks over the public internet, and compress and upload files daily.
- C. Compress and upload both archived files and files uploaded daily using the qsutil -moption.
- D. Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish one Cloud VPN Tunnel to VPC networks over the public internet, and compares and upload files daily using the gsutil-m option.
Answer: C
Explanation:
Explanation/Reference:
NEW QUESTION # 66
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. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) 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. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
- D. 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
Answer: C
Explanation:
Explanation/Reference:
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 # 67
You need to set up Microsoft SQL Server on GCP. Management requires that there's no downtime in case of a data center outage in any of the zones within a GCP region. What should you do?
- A. Set up SQL Server on Compute Engine, using Always On Availability Groups using Windows Failover Clustering. Place nodes in different subnets.
- B. Set up SQL Server Always On Availability Groups using Windows Failover Clustering. Place nodes in different zones.
- C. Configure a Cloud SQL instance with high availability enabled.
- D. Configure a Cloud Spanner instance with a regional instance configuration.
Answer: A
NEW QUESTION # 68
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 dat a. How can you design your logging system to verify authenticity of your logs?
- A. Digitally sign each timestamp and log entry and store the signature.
- B. Create a JSON dump of each log entry and store it in Google Cloud Storage.
- C. Write the log concurrently in the cloud and on premises.
- D. Use a SQL database and limit who can modify the log table.
Answer: A
Explanation:
https://cloud.google.com/storage/docs/access-logs
References: https://cloud.google.com/logging/docs/reference/tools/gcloud-logging
NEW QUESTION # 69
A news teed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed. What is the most likely cause of this problem?
- A. The HTTP Expires header needs to be set to -1 to stop caching.
- B. The session variable is being overwritten in Cloud Datastore.
- C. The session variable is local to just a single instance.
- D. The URL of the API needs to be modified to prevent caching.
Answer: C
Explanation:
Explanation
https://stackoverflow.com/questions/3164280/google-app-engine-cache-list-in-session-variable?rq=1
NEW QUESTION # 70
For this question, refer to the TerramEarth case study.
TerramEarth has equipped unconnected trucks with servers and sensors to collet 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' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
- B. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
- C. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
- D. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
Answer: B
Explanation:
Coldline 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
Topic 3, JencoMart Case Study
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company's core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world's leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S. data centers.
Database
* Oracle Database stores user profiles
* 20 TB
* Complex table structure
* Well maintained, clean data
* Strong backup strategy
* PostgreSQL database stores user credentials
* Single-homed in US West
o No redundancy
o Backed up every 12 hours
* 100% uptime service level agreement (SLA)
* Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:
o Twin, dual core CPUs
o 32GB of RAM
* Twin 250 GB HDD (RAID 1)
* 20 machines in US East Coast, each machine has:
o Single dual-core CPU
o 24 GB of RAM
* Twin 250 GB HDD (RAID 1)
Storage
* Access to shared 100 TB SAN in each location
* Tape backup every week
Business Requirements
* Optimize for capacity during peak periods and value during off-peak periods
* Guarantee service availably and support
* Reduce on-premises footprint and associated financial and environmental impact.
* Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase
* Expand services into Asia.
Technical Requirements
* Assess key application for cloud suitability.
* Modify application for the cloud.
* Move applications to a new infrastructure.
* Leverage managed services wherever feasible
* Sunset 20% of capacity in existing data centers
* Decrease latency in Asia
CEO Statement
JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large global company, we also have a responsibility to the environment through 'green' initiatives and polices.
CTO Statement
The challenges of operating data centers prevents focus on key technologies critical to our long-term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service customers.
CFO Statement
Since its founding JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long-term success. This model will allow us to respond to increasing customer demand during peak and reduce costs.
NEW QUESTION # 71
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. 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. Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which
offer better results. - D. Save a history of recommendations and results of the recommendations in BigQuery, to be used as
training data.
Answer: C
NEW QUESTION # 72
......
Google Professional-Cloud-Architect (Google Certified Professional - Cloud Architect (GCP)) Certification Exam is designed to test the knowledge, skills and expertise of the individuals in designing, developing, and managing secure, scalable, and highly available cloud solutions using Google Cloud Platform (GCP) technologies. Google Certified Professional - Cloud Architect (GCP) certification is ideal for cloud architects, engineers, developers, and IT professionals who want to demonstrate their ability to design and deploy cloud solutions using GCP.
What It Is to Be Google Professional Cloud Architect Certified?
Earning this certificate allows an individual to gain an expert-like understanding of concepts like building the cloud solution architecture and its end-to-end implementation, managing and provisioning the cloud solution infrastructure, and security & compliance of Google cloud.
Accredited professionals are well-versed in everything related to enterprise cloud strategy, architecture planning, and solution designing. They are known to be proficient in the development of methodologies and best practices that are needed for multiple cloud or hybrid environments.
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