PDF (New 2022) Actual Google Professional-Cloud-Architect Exam Questions
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NEW QUESTION 14
You are managing an application deployed on Cloud Run for Anthos, and you need to define a strategy for deploying new versions of the application. You want to evaluate the new code with a subset of production traffic to decide whether to proceed with the rollout. What should you do?
- A. In the Google Cloud Console page for Cloud Run, set up continuous deployment using Cloud Build for the development branch. As part of the Cloud Build trigger, configure the substitution variable TRAFFIC_PERCENTAGE with the percentage of traffic you want directed to a new version.
- B. In the Google Cloud Console, configure Traffic Director with a new Service that points to the new version of the application on Cloud Run. Configure Traffic Director to send a small percentage of traffic to the new version of the application.
- C. Deploy a new service to Cloud Run with the new version. Add a Cloud Load Balancing instance in front of both services.
- D. Deploy a new revision to Cloud Run with the new version. Configure traffic percentage between revisions.
Answer: A
NEW QUESTION 15
For this question, refer to the TerramEarth case study.
The TerramEarth development team wants to create an API to meet the company's business requirements. You want the development team to focus their development effort on business value versus creating a custom framework. Which method should they use?
- A. Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners.
- B. Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners.
- C. Use Google Container Engine with a Django Python container. Focus on an API for the public.
- D. Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public.
- E. Use Google App Engine with the Swagger (open API Specification) framework. Focus on an API for the public.
Answer: B
Explanation:
https://cloud.google.com/endpoints/docs/openapi/about-cloud-endpoints?hl=en_US&_ga=2.21787131.-1712523161.1522785064
https://cloud.google.com/endpoints/docs/openapi/architecture-overview
https://cloud.google.com/storage/docs/gsutil/commands/test
Develop, deploy, protect and monitor your APIs with Google Cloud Endpoints. Using an Open API Specification or one of our API frameworks, Cloud Endpoints gives you the tools you need for every phase of API development.
From scenario:
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 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.
Reference: https://cloud.google.com/certification/guides/cloud-architect/casestudy-terramearth
NEW QUESTION 16
Your solution is producing performance bugs in production that you did not see in staging and test environments. You want to adjust your test and deployment procedures to avoid this problem in the future.
What should you do?
- A. Increase the load on your test and staging environments.
- B. Deploy changes to a small subset of users before rolling out to production.
- C. Deploy smaller changes to production.
- D. Deploy fewer changes to production.
Answer: A
NEW QUESTION 17
Dress4Win would like to become familiar with deploying applications to the cloud by successfully deploying some applications quickly, as is. They have asked for your recommendation.
What should you advise?
- A. Identify self-contained applications with external dependencies as a first move to the cloud.
- B. Suggest moving their in-house databases to the cloud and continue serving requests to on-premise applications.
- C. Recommend moving their message queuing servers to the cloud and continue handling requests to on- premise applications.
- D. Identify enterprise applications with internal dependencies and recommend these as a first move to the cloud.
Answer: B
NEW QUESTION 18
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 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.
- C. 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.
- 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 19
Your company's test suite is a custom C++ application that runs tests throughout each day on Linux virtual machines. The full test suite takes several hours to complete, running on a limited number of on premises servers reserved for testing. Your company wants to move the testing infrastructure to the cloud, to reduce the amount of time it takes to fully test a change to the system, while changing the tests as little as possible. Which cloud infrastructure should you recommend?
- A. Google Cloud Dataproc to run Apache Hadoop jobs to process each test
- B. Google Compute Engine unmanaged instance groups and Network Load Balancer
- C. Google Compute Engine managed instance groups with auto-scaling
- D. Google App Engine with Google Stackdriver for logging
Answer: C
Explanation:
Reference:
https://cloud.google.com/compute/docs/instance-groups/
Google Compute Engine enables users to launch virtual machines (VMs) on demand. VMs can be launched from the standard images or custom images created by users.
Managed instance groups offer autoscaling capabilities that allow you to automatically add or remove instances from a managed instance group based on increases or decreases in load. Autoscaling helps your applications gracefully handle increases in traffic and reduces cost when the need for resources is lower.
NEW QUESTION 20
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. Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application.
- D. 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.
Answer: A
Explanation:
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/
NEW QUESTION 21
You are migrating a Linux-based application from your private data center to Google Cloud. The TerramEarth security team sent you several recent Linux vulnerabilities published by Common Vulnerabilities and Exposures (CVE). You need assistance in understanding how these vulnerabilities could impact your migration. What should you do?
- A. Read the CVEs from the Google Cloud Platform Security Bulletins to understand the impact
- B. Post a question regarding the CVE in a Google Cloud discussion group to get an explanation
- C. Open a support case regarding the CVE and chat with the support engineer.
- D. Read the CVEs from the Google Cloud Status Dashboard to understand the impact.
- E. Post a question regarding the CVE in Stack Overflow to get an explanation
Answer: A,C
Explanation:
https://cloud.google.com/support/bulletins
NEW QUESTION 22
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available
24hrs a day. Your business analysts have experience only with using a SQL interface.
How should you store the data to optimize it for ease of analysis?
- A. Stream data into Google Cloud Datastore
- B. Put flat files into Google Cloud Storage
- C. Load data into Google BigQuery
- D. Insert data into Google Cloud SQL
Answer: C
Explanation:
Explanation/Reference:
Explanation:
BigQuery is Google's serverless, highly scalable, low cost enterprise data warehouse designed to make all your data analysts productive. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL and you don't need a database administrator.
BigQuery enables you to analyze all your data by creating a logical data warehouse over managed, columnar storage as well as data from object storage, and spreadsheets.
References: https://cloud.google.com/bigquery/
NEW QUESTION 23
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 You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customers' wait time for parts You decided to focus on reduction of the 3 weeks aggregate reporting time Which modifications to the company's processes should you recommend?
- A. Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics.
- B. Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics.
- C. Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor.
- D. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics.
Answer: B
Explanation:
The Avro binary format is the preferred format for loading compressed data. Avro data is faster to load because the data can be read in parallel, even when the data blocks are compressed.
Cloud Storage supports streaming transfers with the gsutil tool or boto library, based on HTTP chunked transfer encoding. Streaming data lets you stream data to and from your Cloud Storage account as soon as it becomes available without requiring that the data be first saved to a separate file. Streaming transfers are useful if you have a process that generates data and you do not want to buffer it locally before uploading it, or if you want to send the result from a computational pipeline directly into Cloud Storage.
References: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data
NEW QUESTION 24
Your company is running a stateless application on a Compute Engine instance. The application is used heavily during regular business hours and lightly outside of business hours. Users are reporting that the application is slow during peak hours. You need to optimize the application's performance. What should you do?
- A. Create an instance template from the existing disk. Create a custom image from the instance template.
Create an autoscaled managed instance group from the custom image. - B. Create a snapshot of the existing disk. Create a custom image from the snapshot. Create an autoscaled managed instance group from the custom image.
- C. Create a snapshot of the existing disk. Create an instance template from the snapshot. Create an autoscaled managed instance group from the instance template.
- D. Create a custom image from the existing disk. Create an instance template from the custom image. Create an autoscaled managed instance group from the instance template.
Answer: D
NEW QUESTION 25
You want your Google Kubernetes Engine cluster to automatically add or remove nodes based on CPUload.
What should you do?
- A. Create a deployment and set the maxUnavailable and maxSurge properties. Enable the Cluster Autoscaler using the gcloud command.
- B. Configure a HorizontalPodAutoscaler with a target CPU usage. Enable the Cluster Autoscaler from the GCP Console.
- C. Create a deployment and set the maxUnavailable and maxSurge properties. Enable autoscaling on the cluster managed instance group from the GCP Console.
- D. Configure a HorizontalPodAutoscaler with a target CPU usage. Enable autoscaling on the managed instance group for the cluster using the gcloud command.
Answer: D
NEW QUESTION 26
For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?
- A. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.
- B. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
- C. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use Pig scripts to analyze data.
- D. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
Answer: B
NEW QUESTION 27
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 .....
- D. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
Answer: C
NEW QUESTION 28
For this question, refer to the TerramEarth case study
Your development team has created a structured API to retrieve vehicle dat a. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?
- A. Restrict data access based on the source IP address of the partner systems.
- B. Build or leverage an OAuth-compatible access control system.
- C. Build SAML 2.0 SSO compatibility into your authentication system.
- D. Create secondary credentials for each dealer that can be given to the trusted third party.
Answer: B
Explanation:
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2 Delegate application authorization with OAuth2 Cloud Platform APIs support OAuth 2.0, and scopes provide granular authorization over the methods that are supported. Cloud Platform supports both service-account and user-account OAuth, also called three-legged OAuth.
References: https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
NEW QUESTION 29
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, then push the release again at a quieter period to investigate.
Then use Stackdriver Trace and logging to diagnose the problem. - B. 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.
- C. Work with your ISP 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.
Answer: B
Explanation:
Explanation
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/
NEW QUESTION 30
TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to
20 million 600 byte records a second for 40 TB an hour.
How should you design the data ingestion?
- A. Vehicles stream data directly to Google BigQuery
- B. Vehicles write data directly to GCS
- C. Vehicles continue to write data using the existing system (FTP)
- D. Vehicles write data directly to Google Cloud Pub/Sub
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Streamed data is available for real-time analysis within a few seconds of the first streaming insertion into a
table.
Instead of using a job to load data into BigQuery, you can choose to stream your data into BigQuery one
record at a time by using the tabledata().insertAll() method. This approach enables querying data without
the delay of running a load job.
References: https://cloud.google.com/bigquery/streaming-data-into-bigquery
NEW QUESTION 31
Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize operations. They do not have any existing code for this analysis, so they are exploring all their options.
These options include a mix of batch and stream processing, as they are running some hourly jobs and live-processing some data as it comes in.
Which technology should they use for this?
- A. Google Compute Engine with Google BigQuery
- B. Google Cloud Dataproc
- C. Google Cloud Dataflow
- D. Google Container Engine with Bigtable
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed.
References: https://cloud.google.com/dataflow/
NEW QUESTION 32
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 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
- 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 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
Answer: A
NEW QUESTION 33
Your company runs several databases on a single MySQL instance. They need to take backups of a specific database at regular intervals. The backup activity needs to complete as quickly as possible and cannot be allowed to impact disk performance. How should you configure the storage?
- A. Use gcsfuse to mount a Google Cloud Storage bucket as a volume directly on the instance and write backups to the mounted location using mysqldump
- B. Mount a Local SSD volume as the backup location. After the backup is complete, use gsutil to move the backup to Google Cloud Storage.
- C. Configure a cron job to use the gcloud tool to take regular backups using persistent disk snapshots.
- D. Mount additional persistent disk volumes onto each virtual machine (VM) instance in a RAID10 array and use LVM to create snapshots to send to Cloud Storage.
Answer: A
Explanation:
https://cloud.google.com/storage/docs/gcs-fuse
NEW QUESTION 34
For this question, refer to the Mountkirk Games case study. You are in charge of the new Game Backend Platform architecture. The game communicates with the backend over a REST API.
You want to follow Google-recommended practices. How should you design the backend?
- A. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.
- B. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.
- C. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.
- D. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.
Answer: C
Explanation:
Explanation/Reference:
TerramEarth, A
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.
Company background
TerramEarth was 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, Terram Earth 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 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.
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 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. 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.
NEW QUESTION 35
You want to enable your running Google Container Engine cluster to scale as demand for your application changes.
What should you do?
- A. Update the existing Container Engine cluster with the following command:
gcloud alpha container clusters update mycluster --enable-autoscaling --min-nodes=1 --max-nodes=10 - B. Add additional nodes to your Container Engine cluster using the following command:
gcloud container clusters resize CLUSTER_NAME --size 10 - C. Create a new Container Engine cluster with the following command:
gcloud alpha container clusters create mycluster --enable-autocaling --min-nodes=1 --max-nodes=10 and redeploy your application. - D. Add a tag to the instances in the cluster with the following command:
gcloud compute instances add-tags INSTANCE --tags enable --autoscaling max-nodes-10
Answer: D
Explanation:
Explanation
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler Cluster autoscaling
--enable-autoscaling
Enables autoscaling for a node pool.
Enables autoscaling in the node pool specified by --node-pool or the default node pool if --node-pool is not provided.
Where:
--max-nodes=MAX_NODES
Maximum number of nodes in the node pool.
Maximum number of nodes to which the node pool specified by --node-pool (or default node pool if unspecified) can scale.
NEW QUESTION 36
......
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