[Nov 16, 2022] Fast Exam Updates Professional-Cloud-Architect dumps with PDF Test Engine Practice
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What is the duration, language, and format of Google Professional Cloud Architect Exam
- Format: Multiple choices, multiple answers
- Number of Questions: 50-60
- Passing score: 80%
- Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP
NEW QUESTION 50
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 SQL
- B. Google Cloud Bigtable
- C. Google BigQuery
- D. Google Cloud Storage
Answer: B
NEW QUESTION 51
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 Server Uptime and Error Rate 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 GKE CPU Utilization and Memory Utilization as service level indicators.
Answer: B
Explanation:
Topic 8, Helicopter Racing League Case
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 52
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. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to 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. Have you engineers inspect the data for patterns, and then create an algorithm with rules that 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 53
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. Help the engineer redesign the application to use a distributed user session service that does not rely on websockets and HTTP sessions.
- C. Review the encryption requirements for websocket connections with the security team.
- D. Help the engineer to convert his websocket code to use HTTP streaming.
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 54
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 one project for development, a second for staging and a third for production.
- B. Create one subnetwork for development and another for staging and production.
- C. Create a project for development and test and another for staging and production.
- D. Create a network for development and test and another for staging and production.
Answer: C
Explanation:
Explanation
References: https://cloud.google.com/appengine/docs/standard/go/creating-separate-dev-environments
NEW QUESTION 55
For this question refer to the TerramEarth case study.
Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption.
- A. Opex/capex allocation, LAN changes, capacity planning
- B. Capacity planning, utilization measurement, data center expansion
- C. Capacity planning, TCO calculations, opex/capex allocation
- D. Data Center expansion, TCO calculations, utilization measurement
Answer: C
Explanation:
Reference:
Capacity planning, TCO calculations, opex/capex allocation From the case study, it can conclude that Management (CXO) all concern rapid provision of resources (infrastructure) for growing as well as cost management, such as Cost optimization in Infrastructure, trade up front capital expenditures (Capex) for ongoing operating expenditures (Opex), and Total cost of ownership (TCO)
Topic 1, 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 56
You are creating an App Engine application that uses Cloud Datastore as its persistence layer. You need to retrieve several root entities for which you have the identifiers. You want to minimize the overhead in operations performed by Cloud Datastore. What should you do?
- A. Create the Key object for each Entity and run multiple get operations, one operation for each entity
- B. Create the Key object for each Entity and run a batch get operation
- C. Use the identifiers to create a query filter and run multiple query operations, one operation for each entity
- D. Use the identifiers to create a query filter and run a batch query operation
Answer: D
Explanation:
https://cloud.google.com/datastore/docs/concepts/entities#datastore-datastore-batch-upsert-nodejs
NEW QUESTION 57
You have an application that will run on Compute Engine. You need to design an architecture that takes into account a disaster recovery plan that requires your application to fail over to another region in case of a regional outage. What should you do?
- A. Deploy the application on two Compute Engine instances in the same project but in a different region.
Use the first instance to serve traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster. - B. Deploy the application on two Compute Engine instance groups, each in separate project and a different region. Use the first instance group to server traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster.
- C. Deploy the application on a Compute Engine instance. Use the instance to serve traffic, and use the HTTP load balancing service to fail over to an instance on your premises in case of a disaster.
- D. Deploy the application on two Compute Engine instance groups, each in the same project but in a different region. Use the first instance group to serve traffic, and use the HTTP load balancing service to fail over to the standby instance group in case of a disaster.
Answer: C
NEW QUESTION 58
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's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?
- A. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
- B. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
- C. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a regional bucket and use a Cloud Dataproc cluster to finish the job.
- D. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
Answer: B
Explanation:
Storageguarantees 2 replicates which are geo diverse (100 miles apart) which can get better remote latency and availability.
More importantly, is that multiregional heavily leverages Edge caching and CDNs to provide the content to the end users.
All this redundancy and caching means that Multiregional comes with overhead to sync and ensure consistency between geo-diverse areas. As such, it's much better for write-once-read- many scenarios. This means frequently accessed (e.g. "hot" objects) around the world, such as website content, streaming videos, gaming or mobile applications.
References: https://medium.com/google-cloud/google-cloud-storage-what-bucket-class-for-the- best-performance-5c847ac8f9f2
NEW QUESTION 59
You are tasked with building an online analytical processing (OLAP) marketing analytics and reporting tool. This requires a relational database that can operate on hundreds of terabytes of data. What is the Google- recommended tool for such applications?
- A. BigQuery, because it is designed for large-scale processing of tabular data
- B. Cloud Spanner, because it is globally distributed
- C. Cloud SQL, because it is a fully managed relational database
- D. Cloud Firestore, because it offers real-time synchronization across devices
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/files/BigQueryTechnicalWP.pdf
NEW QUESTION 60
You have an application that makes HTTP requests to Cloud Storage. Occasionally the requests fail with
HTTP status codes of 5xx and 429.
How should you handle these types of errors?
- A. Implement retry logic using a truncated exponential backoff strategy.
- B. Make sure the Cloud Storage bucket is multi-regional for geo-redundancy.
- C. Use gRPC instead of HTTP for better performance.
- D. Monitor https://status.cloud.google.com/feed.atom and only make requests if Cloud Storage is not
reporting an incident.
Answer: A
Explanation:
Explanation/Reference:
Reference https://cloud.google.com/storage/docs/json_api/v1/status-codes
NEW QUESTION 61
You need to deploy an application on Google Cloud that must run on a Debian Linux environment. The application requires extensive configuration in order to operate correctly. You want to ensure that you can install Debian distribution updates with minimal manual intervention whenever they become available. What should you do?
- A. Create a Debian-based Compute Engine instance, install and configure the application, and use OS patch management to install available updates.
- B. Create a Docker container with Debian as the base image. Install and configure the application as part of the Docker image creation process. Host the container on Google Kubernetes Engine and restart the container whenever a new update is available.
- C. Create an instance with the latest available Debian image. Connect to the instance via SSH, and install and configure the application on the instance. Repeat this process whenever a new Google-managed Debian image becomes available.
- D. Create a Compute Engine instance template using the most recent Debian image. Create an instance from this template, and install and configure the application as part of the startup script. Repeat this process whenever a new Google-managed Debian image becomes available.
Answer: A
NEW QUESTION 62
Your company operates nationally and plans to use GCP for multiple batch workloads, including some that are not time-critical. You also need to use GCP services that are HIPAA-certified and manage service costs.
How should you design to meet Google best practices?
- A. Provision standard VMs in the same region to reduce cost. Discontinue use of all GCP services and APIs that are not HIPAA-compliant.
- B. Provisioning preemptible VMs to reduce cost. Disable and then discontinue use of all GCP and APIs that are not HIPAA-compliant.
- C. Provisioning preemptible VMs to reduce cost. Discontinue use of all GCP services and APIs that are not HIPAA-compliant.
- D. Provision standard VMs to the same region to reduce cost. Disable and then discontinue use of all GCP services and APIs that are not HIPAA-compliant.
Answer: B
NEW QUESTION 63
Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations.
Which database type should you use?
- A. Relational
- B. Blobstore
- C. Flat file
- D. NoSQL
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Relational databases were not designed to cope with the scale and agility challenges that face modern applications, nor were they built to take advantage of the commodity storage and processing power available today.
NoSQL fits well for:
Developers are working with applications that create massive volumes of new, rapidly changing data
types - structured, semi-structured, unstructured and polymorphic data.
Incorrect Answers:
D: The Blobstore API allows your application to serve data objects, called blobs, that are much larger than the size allowed for objects in the Datastore service. Blobs are useful for serving large files, such as video or image files, and for allowing users to upload large data files.
References: https://www.mongodb.com/nosql-explained
NEW QUESTION 64
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. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION 65
Case Study: 4 - Dress4Win case study
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model.
Company Background
Dress4win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location.
Databases:
MySQL - user data, inventory, static data
Redis - metadata, social graph, caching
Application servers:
Tomcat - Java micro-services
Nginx - static content
Apache Beam - Batch processing
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN - MySQL databases
NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
Data analysis
Real-time trending calculations
MQ servers:
Messaging
Social notifications
Events
Miscellaneous servers:
Jenkins, monitoring, bastion hosts, security scanners
Business Requirements
Build a reliable and reproducible environment with scaled parity of production. Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud. Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud. Support failover of the production environment to cloud during an emergency. Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model.
For this question, refer to the Dress4Win case study.
Dress4Win has asked you to recommend machine types they should deploy their application servers to. How should you proceed?
- A. Identify the number of virtual cores and RAM associated with the application server virtual machines align them to a custom machine type in the cloud, monitor performance, and scale the machine types up until the desired performance is reached.
- B. Recommend that Dress4Win deploy into production with the smallest instances available, monitor them over time, and scale the machine type up until the desired performance is reached.
- C. Recommend that Dress4Win deploy application servers to machine types that offer the highest RAM to CPU ratio available.
- D. Perform a mapping of the on-premises physical hardware cores and RAM to the nearest machine types in the cloud.
Answer: A
NEW QUESTION 66
An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data. You want to help them find a solution that meets their needs.
What should you do?
- A. Send them a list of online resources about logging best practices
- B. Help them define their requirements and assess viable logging tools
- C. Help them upgrade their current tool to take advantage of any new features
- D. Direct them to download and install the Google StackDriver logging agent
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The Stackdriver Logging agent streams logs from your VM instances and from selected third party software packages to Stackdriver Logging. Using the agent is optional but we recommend it. The agent runs under both Linux and Microsoft Windows.
Note: Stackdriver Logging allows you to store, search, analyze, monitor, and alert on log data and events from Google Cloud Platform and Amazon Web Services (AWS). Our API also allows ingestion of any custom log data from any source. Stackdriver Logging is a fully managed service that performs at scale and can ingest application and system log data from thousands of VMs. Even better, you can analyze all that log data in real time.
References: https://cloud.google.com/logging/docs/agent/installation
NEW QUESTION 67
You are using Cloud Shell and need to install a custom utility for use in a few weeks. Where can you store the file so it is in the default execution path and persists across sessions?
- A. /usr/local/bin
- B. ~/bin
- C. /google/scripts
- D. Cloud Storage
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION 68
Your web application has several VM instances running within a VPC. You want to restrict communications between instances to only the paths and ports you authorize, but you don't want to rely on static IP addresses or subnets because the app can autoscale. How should you restrict communications?
- A. Use Cloud DNS and only allow connections from authorized hostnames
- B. Use service accounts and configure the web application to authorize particular service accounts to have access
- C. Use separate VPCs to restrict traffic
- D. Use firewall rules based on network tags attached to the compute instances
Answer: D
NEW QUESTION 69
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Test Details
This is a 2-hour test comprising two question formats, including multiple-choice and multiple-select items. This exam is available in English and Japanese and costs $200. You can take it as an online proctored exam or in person at the nearest testing center.
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