[2023年10月24日]Professional-Cloud-Architect試験問題集でGoogle練習テスト問題 [Q91-Q112]

[2023年10月24日]Professional-Cloud-Architect試験問題集でGoogle練習テスト問題

最新でリアルなProfessional-Cloud-Architect試験問題集解答

質問 # 91
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 need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games.
Considering the business and technical requirements, what should you do?

• A. Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.
• B. Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.
• C. Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.
• D. Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.

正解：B

質問 # 92
Your applications will be writing their logs to BigQuery for analysis. Each application should have its own table.
Any logs older than 45 days should be removed. You want to optimize storage and follow Google recommended practices. What should you do?

• A. Rely on BigQuery's default behavior to prune application logs older than 45 days
• B. Configure the expiration time for your tables at 45 days
• C. Create a script that uses the BigQuery command line tool (bq) to remove records older than 45 days
• D. Make the tables time-partitioned, and configure the partition expiration at 45 days

正解：B

解説：
Reference:https://cloud.google.com/bigquery/docs/managing-tables

質問 # 93
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.

正解：D

質問 # 94
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 a network for development and test and another for staging and production.
• C. Create one project for development, a second for staging and a third for production.
• D. Create one subnetwork for development and another for staging and production.

正解：C

質問 # 95
Your company sends all Google Cloud logs to Cloud Logging. Your security team wants to monitor the logs. You want to ensure that the security team can react quickly if an anomaly such as an unwanted firewall change or server breach is detected. You want to follow Google-recommended practices. What should you do?

• A. Schedule a cron job with Cloud Scheduler. The scheduled job queries the logs every minute for the relevant events.
• B. Export logs to a Pub/Sub topic, and trigger Cloud Function with the relevant log events.
• C. Export logs to a Cloud Storage bucket, and trigger Cloud Run with the relevant log events.
• D. Export logs to BigQuery, and trigger a query in BigQuery to process the log data for the relevant events.

正解：B

解説：
Reference:
https://cloud.google.com/blog/products/management-tools/automate-your-response-to-a-cloud-logging-event

質問 # 96
Your customer is moving an existing corporate application to Google Cloud Platform from an on-premises data center. The business owners require minimal user disruption. There are strict security team requirements for storing passwords. What authentication strategy should they use?

• A. Use G Suite Password Sync to replicate passwords into Google.
• B. Ask users to set their Google password to match their corporate password.
• C. Federate authentication via SAML 2.0 to the existing Identity Provider.
• D. Provision users in Google using the Google Cloud Directory Sync tool.

正解：A

質問 # 97
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. Flat file
• B. Relational
• C. Blobstore
• D. NoSQL

正解：D

質問 # 98
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 SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
• B. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
• C. Container Engine, Cloud Pub/Sub, and Cloud SQL
• D. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
• E. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery

正解：E

解説：
A real time requires Stream / Messaging so Pub/Sub, Analytics by Big Query.
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
* 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 SQL queries to access at least 10 TB of historical data
* Process files that are regularly uploaded by users' mobile devices
* Use only fully managed services
References:
https://cloud.google.com/solutions/big-data/stream-analytics/

質問 # 99
For this question, refer to the Mountkirk Games case study.
Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a thorough testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way.
How should you design the process?

• A. Use the existing infrastructure to test the GCP-based backend at scale.
• B. Create a set of static environments in GCP to test different levels of load - for example, high, medium, and low.
• C. Build stress tests into each component of your application using resources internal to GCP to simulate load.
• D. Create a scalable environment in GCP for simulating production load.

正解：D

質問 # 100
During a high traffic portion of the day, one of your relational databases crashes, but the replica is never promoted to a master. You want to avoid this in the future. What should you do?

• A. Use a different database.
• B. Implement routinely scheduled failovers of your databases.
• C. Choose larger instances for your database.
• D. Create snapshots of your database more regularly.

正解：B

質問 # 101
The application reliability team at your company has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most
15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss.
Which process should you implement?

• A. * Append metadata to file body.
  * Compress individual files.
  * Name files with serverName-Timestamp.
  * Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket.
  Otherwise, save files to existing bucket
• B. * Append metadata to file body.
  * Compress individual files.
  * Name files with a random prefix pattern.
  * Save files to one bucket
• C. * Batch every 10,000 events with a single manifest file for metadata.
  * Compress event files and manifest file into a single archive file.
  * Name files using serverName-EventSequence.
  * Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket.
  Otherwise, save the single archive file to existing bucket.
• D. * Compress individual files.
  * Name files with serverName-EventSequence.
  * Save files to one bucket
  * Set custom metadata headers for each object after saving.

正解：A

質問 # 102
Auditors visit your teams every 12 months and ask to review all the Google Cloud Identity and Access Management (Cloud IAM) policy changes in the previous 12 months. You want to streamline and expedite the analysis and audit process. What should you do?

• A. Create custom Google Stackdriver alerts and send them to the auditor.
• B. Use cloud functions to transfer log entries to Google Cloud SQL and use ACLS and views to limit an auditor's view.
• C. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.
• D. Enable Google Cloud Storage (GCS) log export to audit logs Into a GCS bucket and delegate access to the bucket.

正解：D

解説：
Reference:
Export the logs to Google Cloud Storage bucket - Archive Storage, as it will not be used for 1 year, price for which is $0.004 per GB per Month. The price for long term storage in BigQuery is $0.01 per GB per Month (250% more). Also for analysis purpose, whenever Auditors are there(once per year), you can use BigQuery and use GCS bucket as external data source. BigQuery supports querying Cloud Storage data from these storage classes:
Standard Nearline Coldline Archive

質問 # 103
Your customer is receiving reports that their recently updated Google App Engine application is taking approximately 30 seconds to load for some of their users. This behavior was not reported before the update. What strategy should you take?

• A. Roll back to an earlier known good release initially, then use Stackdriver Trace and logging to diagnose the problem in a development/test/staging environment.
• B. Work with your ISP to diagnose the problem.
• C. Roll back to an earlier known good release, then push the release again at a quieter period to investigate. Then use Stackdriver Trace and logging to diagnose the problem.
• D. Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application.

正解：A

解説：
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.

質問 # 104
For this question, refer to the JencoMart case study.
JencoMart has built a version of their application on Google Cloud Platform that serves traffic to Asia. You want to measure success against their business and technical goals. Which metrics should you track?

• A. Total visits, error rates, and latency from Asia
• B. The number of character sets present in the database
• C. Error rates for requests from Asia
• D. Latency difference between US and Asia
• E. Total visits and average latency for users in Asia

正解：A

解説：
Explanation
From scenario:
Business Requirements include: Expand services into Asia
Technical Requirements include: Decrease latency in Asia
Topic 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.

質問 # 105
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files. The database is about to run out of storage space.
How can you remediate the problem with the least amount of downtime?

• A. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk
• B. In the Cloud Platform Console, create a snapshot of the persistent disk restore the snapshot to a new larger disk, unmount the old disk, mount the new disk and restart the database service
• C. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine
• D. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux
• E. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.

正解：E

解説：
Explanation/Reference:
Explanation:
On Linux instances, connect to your instance and manually resize your partitions and file systems to use the additional disk space that you added.
Extend the file system on the disk or the partition to use the added space. If you grew a partition on your disk, specify the partition. If your disk does not have a partition table, specify only the disk ID.
sudo resize2fs /dev/[DISK_ID][PARTITION_NUMBER]
where [DISK_ID] is the device name and [PARTITION_NUMBER] is the partition number for the device where you are resizing the file system.
References: https://cloud.google.com/compute/docs/disks/add-persistent-disk

質問 # 106
TerramEarth has equipped all connected trucks with servers and sensors to collect 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 Google BigQuery
• B. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
• C. Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
• D. Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket

正解：C

解説：
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

質問 # 107
Your application needs to process credit card transactions. You want the smallest scope of Payment Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used. How should you design your architecture?

• A. Create separate subnetworks and isolate the components that process credit card data.
• B. Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.
• C. Create a tokenizer service and store only tokenized data.
• D. Create separate projects that only process credit card data.
• E. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.

正解：C

解説：
Explanation
https://cloud.google.com/solutions/pci-dss-compliance-in-gcp

質問 # 108
You want to automate the creation of a managed instance group and a startup script to install the OS package dependencies. You want to minimize the startup time for VMs in the instance group.
What should you do?

• A. Use Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.
• B. Use Puppet to create the managed instance group and install the OS package dependencies.
• C. Use Terraform to create the managed instance group and a startup script to install the OS package dependencies.
• D. Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.

正解：D

解説：
Explanation
"Custom images are more deterministic and start more quickly than instances with startup scripts. However, startup scripts are more flexible and let you update the apps and settings in your instances more easily."
https://cloud.google.com/compute/docs/instance-templates/create-instance-templates#using_custom_or_public_i

質問 # 109
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



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.
JencoMart wants to move their User Profiles database to Google Cloud Platform.
Which Google Database should they use?

• A. Cloud Spanner
• B. Google Cloud Datastore
• C. Google Cloud SQL
• D. Google BigQuery

正解：B

解説：
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

質問 # 110
Your company just finished a rapid lift and shift to Google Compute Engine for your compute needs. You have another 9 months to design and deploy a more cloud-native solution.
Specifically, you want a system that is no-ops and auto-scaling. Which two compute products should you choose? Choose 2 answers

• A. Compute Engine with managed instance groups
• B. Compute Engine with custom instance types
• C. Google Container Engine with containers
• D. Compute Engine with containers
• E. Google App Engine Standard Environment

正解：C、E

解説：
B: With Container Engine, Google will automatically deploy your cluster for you, update, patch, secure the nodes.
Kubernetes Engine's cluster autoscaler automatically resizes clusters based on the demands of the workloads you want to run.
C: Solutions like Datastore, BigQuery, AppEngine, etc are truly NoOps.
App Engine by default scales the number of instances running up and down to match the load, thus providing consistent performance for your app at all times while minimizing idle instances and thus reducing cost.
Note: At a high level, NoOps means that there is no infrastructure to build out and manage during usage of the platform. Typically, the compromise you make with NoOps is that you lose control of the underlying infrastructure.
References: https://www.quora.com/How-well-does-Google-Container-Engine-support-Google- Cloud-Platform%E2%80%99s-NoOps-claim

質問 # 111
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. Write a schema migration plan to denormalize data for better performance in BigQuery.
• B. Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.
• 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. Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.
• E. Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.

正解：A、E

質問 # 112
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