Professional-Cloud-Architect Dumps Special Discount for limited time Try FOR FREE
Professional-Cloud-Architect Dumps for success in Actual Exam Feb-2023]
NEW QUESTION 145
The development team has provided you with a Kubernetes Deployment file. You have no infrastructure yet and need to deploy the application. What should you do?
- A. Use kubect1 to create a Kubernetes cluster. Use kubect1 to create the deployment.
- B. Use kubect1 to create a Kubernetes cluster. Use Deployment Manager to create the deployment.
- C. Use gcloud to create a Kubernetes cluster. Use kubect1 to create the deployment.
- D. Use gcloud to create a Kubernetes cluster. Use Deployment Manager to create the deployment.
Answer: C
Explanation:
Explanation
https://cloud.google.com/kubernetes-engine/docs/how-to/creating-a-cluster
NEW QUESTION 146
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. Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
- B. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
- C. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery
- D. Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket
Answer: A
Explanation:
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
NEW QUESTION 147
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. Load data into Google BigQuery.
- B. Put flat files into Google Cloud Storage.
- C. Stream data into Google Cloud Datastore.
- D. Insert data into Google Cloud SQL.
Answer: A
Explanation:
Google Big Query is for multi peta byte storage , HA(High availability) which means 24 hours, SQL interface .
https://medium.com/google-cloud/the-12-components-of-google-bigquery-c2b49829a7c7
https://cloud.google.com/solutions/bigquery-data-warehouse
https://cloud.google.com/bigquery/
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.
NEW QUESTION 148
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. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
- C. 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.
- D. 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 .....
Answer: D
NEW QUESTION 149
You have created several preemptible Linux virtual machine instances using Google Compute Engine. You want to properly shut down your application before the virtual machines are preempted. What should you do?
- A. Create a shutdown script named k99.shutdown in the /etc/rc.6.d/ directory.
- B. Create a shutdown script and use it as the value for a new metadata entry with the key shutdown-script in the Cloud Platform Console when you create the new virtual machine instance.
- C. Create a shutdown script, registered as a xinetd service in Linux, and use the gcloud compute instances add-metadata command to specify the service URL as the value for a new metadata entry with the key shutdown-script-url
- D. Create a shutdown script registered as a xinetd service in Linux and configure a Stackdnver endpoint check to call the service.
Answer: B
Explanation:
Explanation
A startup script, or a shutdown script, is specified through the metadata server, using startup script metadata keys.
Reference: https://cloud.google.com/compute/docs/startupscript
https://cloud.google.com/compute/docs/shutdownscript
NEW QUESTION 150
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. Cloud Spanner, because it is globally distributed
- B. Cloud Firestore, because it offers real-time synchronization across devices
- C. Cloud SQL, because it is a fully managed relational database
- D. BigQuery, because it is designed for large-scale processing of tabular data
Answer: D
Explanation:
Reference: https://cloud.google.com/files/BigQueryTechnicalWP.pdf
NEW QUESTION 151
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. Help the engineer to convert his websocket code to use HTTP streaming.
- B. Help the engineer redesign the application to use a distributed user session service that does not rely on websockets and HTTP sessions.
- C. Meet with the cloud operations team and the engineer to discuss load balancer options.
- D. Review the encryption requirements for websocket connections with the security team.
Answer: C
Explanation:
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.
NEW QUESTION 152
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 and average latency for users in Asia
- B. Total visits, error rates, and latency from Asia
- C. The number of character sets present in the database
- D. Error rates for requests from Asia
- E. Latency difference between US and Asia
Answer: A
NEW QUESTION 153
You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to BigQuery.
What should you do to fix the script?
- A. Run your script on a new virtual machine with the BigQuery access scope enabled
- B. Install the latest BigQuery API client library for Python
- C. Create a new service account with BigQuery access and execute your script with that user
- D. Install the bq component for gcloud with the command gcloud components install bq.
Answer: A
NEW QUESTION 154
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
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.
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 and average latency for users in Asia
- B. Total visits, error rates, and latency from Asia
- C. The number of character sets present in the database
- D. Error rates for requests from Asia
- E. Latency difference between US and Asia
Answer: A
Explanation:
From scenario:
Business Requirements include: Expand services into Asia
Technical Requirements include: Decrease latency in Asia
NEW QUESTION 155
Your company uses the Firewall Insights feature in the Google Network Intelligence Center. You have several firewall rules applied to Compute Engine instances. You need to evaluate the efficiency of the applied firewall ruleset. When you bring up the Firewall Insights page in the Google Cloud Console, you notice that there are no log rows to display. What should you do to troubleshoot the issue?
- A. Verify that your user account is assigned the compute.networkAdmin Identity and Access Management (IAM) role.
- B. Enable Firewall Rules Logging for the firewall rules you want to monitor.
- C. Install the Google Cloud SDK, and verify that there are no Firewall logs in the command line output.
- D. Enable Virtual Private Cloud (VPC) flow logging.
Answer: B
Explanation:
Reference: https://cloud.google.com/network-intelligence-center/docs/firewall-insights/how-to/using-firewall- insights
NEW QUESTION 156
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. Container Engine, Cloud Pub/Sub, and Cloud SQL
- B. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
- C. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
- D. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
- E. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
Answer: E
Explanation:
Explanation
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/
Topic 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.
NEW QUESTION 157
You are using Cloud CDN to deliver static HTTP(S) website content hosted on a Compute Engine instance group. You want to improve the cache hit ratio.
What should you do?
- A. Make sure the HTTP(S) header "Cache-Region" points to the closest region of your users.
- B. Replicate the static content in a Cloud Storage bucket. Point CloudCDN toward a load balancer on that bucket.
- C. Shorten the expiration time of the cached objects.
- D. Customize the cache keys to omit the protocol from the key.
Answer: D
Explanation:
Reference:
https://cloud.google.com/cdn/docs/bestpractices#
using_custom_cache_keys_to_improve_cache_hit_ratio
NEW QUESTION 158
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. Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
- B. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
- C. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery
- D. Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket
Answer: A
Explanation:
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.
Reference: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION 159
You have developed an application using Cloud ML Engine that recognizes famous paintings from uploaded images. You want to test the application and allow specific people to upload images for the next 24 hours. Not all users have a Google Account. How should you have users upload images?
- A. Have users upload the images to Cloud Storage. Protect the bucket with a password that expires after 24 hours.
- B. Have users upload the images to Cloud Storage using a signed URL that expires after 24 hours.
- C. Create an App Engine web application where users can upload images. Configure App Engine to disable the application after 24 hours. Authenticate users via Cloud Identity.
- D. Create an App Engine web application where users can upload images for the next 24 hours. Authenticate users via Cloud Identity.
Answer: A
NEW QUESTION 160
Your organization has a 3-tier web application deployed in the same network on Google Cloud Platform. Each tier (web, API, and database) scales independently of the others Network traffic should flow through the web to the API tier and then on to the database tier. Traffic should not flow between the web and the database tier.
How should you configure the network?
- A. Add tags to each tier and set up routes to allow the desired traffic flow.
- B. Add each tier to a different subnetwork.
- C. Set up software based firewalls on individual VMs.
- D. Add tags to each tier and set up firewall rules to allow the desired traffic flow.
Answer: D
Explanation:
Explanation
https://aws.amazon.com/blogs/aws/building-three-tier-architectures-with-security-groups/ Google Cloud Platform(GCP) enforces firewall rules through rules and tags. GCP rules and tags can be defined once and used across all regions.
References: https://cloud.google.com/docs/compare/openstack/
https://aws.amazon.com/it/blogs/aws/building-three-tier-architectures-with-security-groups/
NEW QUESTION 161
The current Dress4win system architecture has high latency to some customers because it is located in one data center.
As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute it's system architecture to multiple locations when Google cloud platform.
Which approach should they use?
- A. Use regional managed instance groups and a global load balancer to increase reliability by providing automatic failover between zones in different regions.
- B. Use regional managed instance groups and a global load balancer to increase performance because the regional managed instance group can grow instances in each region separately based on traffic.
- C. Use a global load balancer with a set of virtual machines that forward the requests to a closer group of virtual machines managed by your operations team.
- D. Use a global load balancer with a set of virtual machines that forward the requests to a closer group of virtual machines as part of a separate managed instance groups.
Answer: B
Explanation:
Reference:
NEW QUESTION 162
Your company is moving 75 TB of data into Google Cloud. You want to use Cloud Storage and follow Google-recommended practices. What should you do?
- A. Move your data onto a Transfer Appliance. Use a Transfer Appliance Rehydrator to decrypt the data into Cloud Storage.
- B. Install gsutil on each server that contains data. Use resumable transfers to upload the data into Cloud Storage.
- C. Move your data onto a Transfer Appliance. Use Cloud Dataprep to decrypt the data into Cloud Storage.
- D. Install gsutil on each server containing data. Use streaming transfers to upload the data into Cloud Storage.
Answer: B
Explanation:
https://cloud.google.com/solutions/transferring-big-data-sets-to-gcp
NEW QUESTION 163
For this question, refer to the Dress4Win case study. Dress4Win is expected to grow to 10 times its size in 1 year with a corresponding growth in data and traffic that mirrors the existing patterns of usage. The CIO has set the target of migrating production infrastructure to the cloud within the next 6 months. How will you configure the solution to scale for this growth without making major application changes and still maximize the ROI?
- A. Migrate the web application layer to App Engine, and MySQL to Cloud Datastore, and NAS to Cloud Storage. Deploy RabbitMQ, and deploy Hadoop servers using Deployment Manager.
- B. Implement managed instance groups for the Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Cloud Storage.
- C. Implement managed instance groups for Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Compute Engine with Persistent Disk storage.
- D. Migrate RabbitMQ to Cloud Pub/Sub, Hadoop to BigQuery, and NAS to Compute Engine with Persistent Disk storage. Deploy Tomcat, and deploy Nginx using Deployment Manager.
Answer: B
Explanation:
Topic 6, TerramEarth Case 2
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.
NEW QUESTION 164
A news teed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed. What is the most likely cause of this problem?
- A. The session variable is local to just a single instance.
- B. The URL of the API needs to be modified to prevent caching.
- C. The HTTP Expires header needs to be set to -1 to stop caching.
- D. The session variable is being overwritten in Cloud Datastore.
Answer: A
Explanation:
Explanation
https://stackoverflow.com/questions/3164280/google-app-engine-cache-list-in-session-variable?rq=1
NEW QUESTION 165
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. Create a tokenizer service and store only tokenized data.
- C. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.
- D. Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.
- E. Create separate projects that only process credit card data.
Answer: B
Explanation:
Reference:
https://cloud.google.com/solutions/pci-dss-compliance-in-gcp
NEW QUESTION 166
For this question, refer to the Dress4Win case study.
Dress4Win has configured a new uptime check with Google Stackdriver for several of their legacy services. The Stackdriver dashboard is not reporting the services as healthy. What should they do?
- A. Configure their load balancer to pass through the User-Agent HTTP header when the value matches GoogleStackdriverMonitoring-UptimeChecks (https://cloud.google.com/monitoring)
- B. Configure their legacy web servers to allow requests that contain user-Agent HTTP header when the value matches GoogleStackdriverMonitoring- UptimeChecks (https://cloud.google.com/monitoring)
- C. In the Cloud Platform Console download the list of the uptime servers' IP addresses and create an inbound firewall rule
- D. Install the Stackdriver agent on all of the legacy web servers.
Answer: A
NEW QUESTION 167
......
Accurate Professional-Cloud-Architect Answers 365 Days Free Updates: https://www.prep4sureexam.com/Professional-Cloud-Architect-dumps-torrent.html
Realistic Professional-Cloud-Architect 100% Pass Guaranteed Download Exam Q&A: https://drive.google.com/open?id=1W-AQe2dsGfbu-9uFPduauwNveUw_yGLu