RELIABLE PROFESSIONAL-CLOUD-ARCHITECT DUMPS SHEET & DETAIL PROFESSIONAL-CLOUD-ARCHITECT EXPLANATION

Reliable Professional-Cloud-Architect Dumps Sheet & Detail Professional-Cloud-Architect Explanation

Reliable Professional-Cloud-Architect Dumps Sheet & Detail Professional-Cloud-Architect Explanation

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To become a Google Certified Professional-Cloud-Architect, candidates must pass a rigorous exam that covers topics such as designing and planning a cloud solution architecture, managing and provisioning cloud infrastructure, optimizing and securing a solution, and analyzing and optimizing technical and business processes. Professional-Cloud-Architect exam also assesses the candidate's ability to use Google Cloud Platform tools and services effectively.

Google Professional-Cloud-Architect exam is a certification that verifies the expertise of an individual in the field of cloud computing with a focus on Google Cloud Platform (GCP). Professional-Cloud-Architect Exam is designed for professionals who are responsible for designing, developing, and managing cloud solutions using GCP. Google Certified Professional - Cloud Architect (GCP) certification validates the skills required to handle complex enterprise-level solutions that are secure, scalable, and reliable.

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q85-Q90):

NEW QUESTION # 85
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 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. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
  • B. Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
  • C. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
  • D. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.

Answer: D

Explanation:
Answer should be B - Why would you collect data in Coldline when the purpose is to collect data to analysis down the line and cost wise it should be similar to store such large volume in BQ instead of COldline given the Data access cost involved. ML can be directly done on BQ instead of pulling this data from Coldline into another DB and then applying ML algos on the data which will be more expensive to achieve


NEW QUESTION # 86
Your organization has a 3-tier web application deployed in the same network on Google Cloud Platform. Each tier (web, API, and database) scales independently of the others Network traffic should flow through the web to the API tier and then on to the database tier. Traffic should not flow between the web and the database tier. How should you configure the network?

  • A. Add tags to each tier and set up firewall rules to allow the desired traffic flow.
  • B. Set up software based firewalls on individual VMs.
  • C. Add each tier to a different subnetwork.
  • D. Add tags to each tier and set up routes to allow the desired traffic flow.

Answer: A

Explanation:
https://aws.amazon.com/blogs/aws/building-three-tier-architectures-with-security-groups/ Google Cloud Platform(GCP) enforces firewall rules through rules and tags. GCP rules and tags can be defined once and used across all regions.
References:
https://cloud.google.com/docs/compare/openstack/
https://aws.amazon.com/it/blogs/aws/building-three-tier-architectures-with-security-groups/


NEW QUESTION # 87
Your customer is moving their corporate applications to Google Cloud Platform. The security team wants detailed visibility of all projects in the organization. You provision the Google Cloud Resource Manager and set up yourself as the org admin. What Google Cloud Identity and Access Management (Cloud IAM) roles should you give to the security team'?

  • A. Org viewer, project owner
  • B. Org admin, project browser
  • C. Org viewer, project viewer
  • D. Project owner, network admin

Answer: C

Explanation:
Explanation
https://cloud.google.com/iam/docs/using-iam-securely


NEW QUESTION # 88
For this question, refer to the Dress4Win case study.
Dress4Win has asked you for advice on how to migrate their on-premises MySQL deployment to the cloud. They want to minimize downtime and performance impact to their on-premises solution during the migration. Which approach should you recommend?

  • A. Create a new MySQL cluster in the cloud, configure applications to begin writing to both on-premises and cloud MySQL masters, and destroy the original cluster at cutover.
  • B. Setup a MySQL replica server/slave in the cloud environment, and configure it for asynchronous replication from the MySQL master server on-premises until cutover.
  • C. Create a dump of the MySQL replica server into the cloud environment, load it into: Google Cloud Datastore, and configure applications to read/write to Cloud Datastore at cutover.
  • D. Create a dump of the on-premises MySQL master server, and then shut it down, upload it to the cloud environment, and load into a new MySQL cluster.

Answer: B


NEW QUESTION # 89
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. Which managed storage option meets Mountkirk's technical requirement for storing game activity in a time series database service?

  • A. BigQuery
  • B. Cloud Bigtable
  • C. Cloud Spanner
  • D. Cloud Datastore

Answer: B


NEW QUESTION # 90
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