[Nov-2021] Professional-Cloud-Architect exam torrent Google study guide
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Introduction to Google Professional Cloud Architect Exam
Google Professional Cloud Architect Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Professional Cloud Architect in the IT industry.
After passing this exam, candidates get a certificate from Google that helps them to demonstrate their proficiency in Google Professional Cloud Architect to their clients and employers.
NEW QUESTION 138
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. Export logs to a Pub/Sub topic, and trigger Cloud Function with the relevant log events.
- B. Export logs to BigQuery, and trigger a query in BigQuery to process the log data for the relevant events.
- C. Schedule a cron job with Cloud Scheduler. The scheduled job queries the logs every minute for the relevant events.
- D. Export logs to a Cloud Storage bucket, and trigger Cloud Run with the relevant log events.
Answer: A
NEW QUESTION 139
Your organization has 5 TB of private data on premises. You need to migrate the data to Cloud Storage. You want to maximize the data transfer speed. How should you migrate the data?
- A. Use GCS REST API.
- B. Use Storage Transfer Service.
- C. Use gsutil.
- D. Use gcloud.
Answer: C
Explanation:
A is correct because gsutil gives you access to write data to Cloud Storage.
B is not correct because gcloud is the command-line interface for common platform tasks and does not include accessing Cloud Storage.
C is not correct because the data size would require a resumable upload, and that does not meet the requirement of maximizing the data transfer speed.
D is not correct because Storage Transfer Service is for importing online data, not on-premises.
Your data source can be an Amazon Simple Storage Service (Amazon S3) bucket, an HTTP/HTTPS location, or a Cloud Storage bucket.
https://cloud.google.com/storage/docs/gsutil
https://cloud.google.com/sdk/gcloud/
https://cloud.google.com/storage/docs/json_api/v1/how-tos/upload
https://cloud.google.com/storage/docs/uploading-objects
https://cloud.google.com/storage-transfer/docs/overview
NEW QUESTION 140
For this question, refer to the TerramEarth case study.
To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections. What should you do?
- A. Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in us, eu, and asia. Run the ETL process using the data in the bucket.
- B. Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket.
- C. Directly transfer the files to a different Google Cloud Regional Storage bucket location in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket.
- D. Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket.
Run the ETL process using data in the bucket.
Answer: C
NEW QUESTION 141
The database administration team has asked you to help them improve the performance of their new database server running on Google Compute Engine. The database is for importing and normalizing their performance statistics and is built with MySQL running on Debian Linux. They have an n1-standard-8 virtual machine with 80 GB of SSD persistent disk. What should they change to get better performance from this system?
- A. Increase the virtual machine's memory to 64 GB.
- B. Dynamically resize the SSD persistent disk to 500 GB.
- C. Create a new virtual machine running PostgreSQL.
- D. Modify all of their batch jobs to use bulk inserts into the database.
- E. Migrate their performance metrics warehouse to BigQuery.
Answer: B
NEW QUESTION 142
You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore.
What should you do?
- A. Upload the configuration file the App Engine's default Cloud Storage bucket, and have App Engine detect the new indexes
- B. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file
- C. Point gcloud datastore create-indexes to your configuration file
- D. Create an HTTP request to the built-in python module to send the index configuration file to your application
Answer: C
NEW QUESTION 143
You are migrating your on-premises solution to Google Cloud in several phases. You will use Cloud VPN to maintain a connection between your on-premises systems and Google Cloud until the migration is completed. You want to make sure all your on-premise systems remain reachable during this period. How should you organize your networking in Google Cloud?
- A. Use the same IP range on Google Cloud as you use on-premises for your primary IP range and use a secondary range that does not overlap with the range you use on-premises
- B. Use an IP range on Google Cloud that does not overlap with the range you use on-premises
- C. Use the same IP range on Google Cloud as you use on-premises
- D. Use an IP range on Google Cloud that does not overlap with the range you use on-premises for your primary IP range and use a secondary range with the same IP range as you use on-premises
Answer: D
NEW QUESTION 144
For this question, refer to the Dress4Win case study. Considering the given business requirements, how
would you automate the deployment of web and transactional data layers?
- A. Migrate Nginx and Tomcat to App Engine. Deploy a Cloud Datastore server to replace the MySQL
server in a high-availability configuration. Deploy Jenkins to Compute Engine using Cloud Launcher. - B. Deploy Nginx and Tomcat using Cloud Launcher. Deploy a MySQL server using Cloud Launcher.
Deploy Jenkins to Compute Engine using Cloud Deployment Manager scripts. - C. Migrate Nginx and Tomcat to App Engine. Deploy a MySQL server using Cloud Launcher. Deploy
Jenkins to Compute Engine using Cloud Launcher. - D. Deploy Nginx and Tomcat using Cloud Deployment Manager to Compute Engine. Deploy a Cloud SQL
server to replace MySQL. Deploy Jenkins using Cloud Deployment Manager.
Answer: A
NEW QUESTION 145
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 BigQuery
- C. Google Cloud Bigtable
- D. Google Cloud Storage
Answer: C
Explanation:
It is time-series data, So Big Table.
https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION 146
Your company wants to try out the cloud with low risk. They want to archive approximately 100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long- term disaster recovery backup.
Which two steps should you take? Choose 2 answers.
- A. Insert logs into Google Cloud Bigtable
- B. Import logs into Google Stackdriver
- C. Load logs into Google Cloud SQL
- D. Upload log files into Google Cloud Storage
- E. Load logs into Google BigQuery
Answer: D,E
NEW QUESTION 147
You are deploying a PHP App Engine Standard service with SQL as the backend. You want to minimize the number of queries to the database.
What should you do?
- A. Set the memcache service level to shared. Create a key called "cached-queries", and return database values from the key before using a query to Cloud SQL.
- B. Set the memcache service level to dedicated. Create a key from the hash of the query, and return database values from memcache before issuing a query to Cloud SQL.
- C. Set the memcache service level to shared. Create a cron task that runs every minute to save all expected queries to a key called "cached-queries".
- D. Set the memcache service level to dedicated. Create a cron task that runs every minute to populate the cache with keys containing query results.
Answer: B
Explanation:
https://cloud.google.com/appengine/docs/standard/php/memcache/using
NEW QUESTION 148
For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost.
Which two actions should you take?
- A. Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Standard", and Action: "Set to Coldline", and create a second GCS life-cycle rule with Age: "365", Storage Class: "Nearline", and Action:
"Delete". - B. Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Coldline", and Action: "Set to Nearline", and create a second GCS life-cycle rule with Age: "91", Storage Class: "Coldline", and Action: "Set to Nearline".
- C. Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Standard", and Action: "Set to Coldline", and create a second GCS life-cycle rule with Age: "365", Storage Class: "Coldline", and Action:
"Delete". - D. Create a Cloud Storage lifecycle rule with Age: "90", Storage Class: "Standard", and Action: "Set to Nearline", and create a second GCS life-cycle rule with Age: "91", Storage Class: "Nearline", and Action:
"Set to Coldline".
Answer: C
NEW QUESTION 149
Case Study: 6 - TerramEarth
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
Decrease unplanned vehicle downtime to less than 1 week.
* Support the dealer network with more data on how their customers use their equipment to better
* position new products and services
Have the ability to partner with different companies - especially with seed and fertilizer suppliers
* in the fast-growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east
* coast.
Create a backup strategy.
* Increase security of data transfer from equipment to the datacenter.
* Improve data in the data warehouse.
* Use customer and equipment data to anticipate customer needs.
* Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
* - 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
* - Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server
* - RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth. Considering the TerramEarth business and technical requirements, what should you do?
- A. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.
- B. Replace the existing data warehouse with BigQuery. Use federated data sources.
- C. Replace the existing data warehouse with BigQuery. Use table partitioning.
- D. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.
Answer: B
NEW QUESTION 150
You created a pipeline that can deploy your source code changes to your infrastructure in instance groups for self-healing. One of the changes negatively affects your key performance indicator. You are not sure how to fix it, and investigation could take up to a week.
What should you do?
- A. Log into the servers with the bad code change, and swap in the previous code
- B. Change the instance group template to the previous one, and delete all instances
- C. Revert the source code change, and rerun the deployment pipeline
- D. Log in to a server, and iterate on the fox locally
Answer: C
NEW QUESTION 151
You are helping the QA team to roll out a new load-testing tool to test the scalability of your primary cloud services that run on Google Compute Engine with Cloud Bigtable.
Which three requirements should they include? Choose 3 answers
- A. Create a separate Google Cloud project to use for the load-testing environment.
- B. Instrument the production services to record every transaction for replay by the load-testing tool.
- C. Ensure all third-party systems your services use are capable of handling high load.
- D. Schedule the load-testing tool to regularly run against the production environment.
- E. Ensure that the load tests validate the performance of Cloud Bigtable.
- F. Instrument the load-testing tool and the target services with detailed logging and metrics collection.
Answer: A,E,F
Explanation:
A (Correct answer) - Ensure that the load tests validate the performance of Cloud Bigtable.
At first thought, it seems Bigtable is managed service seamlessly scaling, there is no need to load test it. But you probably already know, there are many factors impacting the Bigtable performance. The common one of them, poorly designed table structure.
With poorly performed BigTable, the cluster would continue to scale, add more and more nodes, as the load increasing, cost more and more. Not matter how good is the cluster managed, this is exact situation that needs to prevent. In fact, tests validate the performance of Cloud Bigtable is one of the most important testing goals for BigTable performance.
B (Correct answer) - Create a separate Google Cloud project to use for the load-testing environment.
This is a good practice for least privileges and separation of responsibilities.
C - Schedule the load-testing tool to regularly run against the production environment.
You should not do load testing RGULARLY against production environment; In fact, somebody has suggested Load test in production should avoid entirely.
D - Ensure all third-party systems your services used are capable of handling high load This is out of scope - it's beyond your control. You may test them, but you cannot control them. If you have doubts the third-party dependencies causing the overall performance issue, you may use integration test to identify and isolate them. This should not be your focus.
E - Instrument the production services to record every transaction for replay by the load-testing tool.
This would be way too much: not only the detailed instrumentation may impact the production performance, it's also possible the instrumentation itself distorts the test results - remember, Bigtable is for multiple terabyte or even petabyte NoSQL database for high throughput with low latency read and write.
F (Correct answer) - Instrument the load-testing tool and the target services with detailed logging and metrics collection.
This is normal requirement and practice for load testing to collect testing results with detailed measurable metric and historical logs otherwise load testing would be meaningless More Resource Understanding Cloud Bigtable Performance
https://cloud.google.com/bigtable/docs/performance
NEW QUESTION 152
You need to evaluate your team readiness for a new GCP project. You must perform the evaluation and create a skills gap plan incorporates the business goal of cost optimization. Your team has deployed two GCP projects successfully to date. What should you do?
- A. Allocate budget for team training. Create a roadmap for your team to achieve Google Cloud certification based on job role.
- B. Allocate budget to hire skilled external consultants. Create a roadmap for your team to achieve Google Cloud certification based on job role.
- C. Allocate budget to hire skilled external consultants. Set a deadline for the new GCP project.
- D. Allocate budget for team training. Set a deadline for the new GCP project.
Answer: A
Explanation:
https://services.google.com/fh/files/misc/cloud_center_of_excellence.pdf
NEW QUESTION 153
You have an application deployed on Kubernetes Engine using a Deployment named echo- deployment. The deployment is exposed using a Service called echo-service. You need to perform an update to the application with minimal downtime to the application. What should you do?
- A. Update the service yaml file which the new container image. Use kubectl delete service/ echo- service and kubectl create <yaml-file>
- B. Update the deployment yaml file with the new container image. Use kubectl delete deployment/echo-deployment and kubectl create <yaml-file>
- C. Use kubectl set image deployment/echo-deployment <new-image>
- D. Use the rolling update functionality of the Instance Group behind the Kubernetes cluster
Answer: D
NEW QUESTION 154
You are developing a globally scaled frontend for a legacy streaming backend data API. This API expects events in strict chronological order with no repeat data for proper processing.
Which products should you deploy to ensure guaranteed-once FIFO (first-in, first-out) delivery of data?
- A. Cloud Pub/Sub to Cloud DataFlow
- B. Cloud Pub/Sub to Cloud SQL
- C. Cloud Pub/Sub to Stackdriver
- D. Cloud Pub/Sub alone
Answer: B
Explanation:
Explanation/Reference:
Reference https://cloud.google.com/pubsub/docs/ordering
NEW QUESTION 155
Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs. What should they do?
- A. Use separate backend pools for each API path behind the load balancer.
- B. Configure a new load balancer for the new version of the API.
- C. Have the old API forward traffic to the new API based on the path.
- D. Reconfigure old clients to use a new endpoint for the new API.
Answer: A
Explanation:
Reference:
https://cloud.google.com/endpoints/docs/openapi/lifecycle-management
NEW QUESTION 156
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 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.
- 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 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 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.
Answer: B
NEW QUESTION 157
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. Error rates for requests from Asia
- B. Total visits, error rates, and latency from Asia
- C. Total visits and average latency for users in Asia
- D. Latency difference between US and Asia
- E. The number of character sets present in the database
Answer: C
NEW QUESTION 158
For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?
- A. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
- B. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.
- C. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
- D. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
Answer: A
NEW QUESTION 159
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 separate VPCs to restrict traffic
- B. Use firewall rules based on network tags attached to the compute instances
- C. Use service accounts and configure the web application to authorize particular service accounts to have access
- D. Use Cloud DNS and only allow connections from authorized hostnames
Answer: B
NEW QUESTION 160
For this question, refer to the TerramEarth case study.
To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections. What should you do?
- A. Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in us, eu, and asia. Run the ETL process using the data in the bucket.
- B. Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket.
- C. Directly transfer the files to a different Google Cloud Regional Storage bucket location in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket.
- D. Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket.
Answer: D
Explanation:
Explanation
From scenario: They currently have over 500 dealers and service centers in 100 countries.
Multi-Regional Storage is appropriate for storing data that is frequently accessed ("hot" objects), such as serving website content, interactive workloads, or data supporting mobile and gaming applications.
Multi-Regional Storage data has the most availability compared to other storage classes.
Multi-Regional Storage is geo-redundant, which means Cloud Storage stores your data redundantly in at least two geographic places separated by at least 100 miles within the multi-regional location of the bucket.
Geo-redundancy occurs asynchronously, but Multi-Regional Storage data is redundant within at least one geographic place as soon as you upload it. Like all Cloud Storage data, it is also immediately accessible worldwide.
References: https://cloud.google.com/storage/docs/storage-classes#multi-regional
NEW QUESTION 161
For this question, refer to the Dress4Win case study.
As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?
- A. Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer's unique ID.
- B. Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer's ID to their image files.
- C. Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file's owner attribute, ensuring privacy of images.
- D. Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer's ID and their image files.
Answer: D
NEW QUESTION 162
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