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NEW QUESTION 26
Your customer runs a web service used by e-commerce sites to offer product recommendations to users.
the company has begun experimenting with a machine learning model on Google Cloud Platform to
improve the quality of results.
What should the customer do to improve their model's results over time?
- A. Save a history of recommendations and results of the recommendations in BigQuery, to be used as
training data. - B. Export Cloud Machine Learning Engine performance metrics from Stackdriver to BigQuery, to be used
to analyze the efficiency of the model. - C. Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which
offer better results. - D. Monitor Compute Engine announcements for availability of newer CPU architectures, and deploy the
model to them as soon as they are available for additional performance.
Answer: C
NEW QUESTION 27
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. Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.
- B. Use Deployment Manager to create the managed instance group and Ansible to 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. Use Puppet to create the managed instance group and install the OS package dependencies.
Answer: A
Explanation:
Reference:
"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_images_in_your_instance_templates
NEW QUESTION 28
Case Study: 4 - Dress4Win case study
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model.
Company Background
Dress4win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location.
Databases:
MySQL - user data, inventory, static data
* Redis - metadata, social graph, caching
* Application servers:
Tomcat - Java micro-services
* Nginx - static content
* Apache Beam - Batch processing
* Storage appliances:
iSCSI for VM hosts
* Fiber channel SAN - MySQL databases
* NAS - image storage, logs, backups
* Apache Hadoop/Spark servers:
Data analysis
* Real-time trending calculations
* MQ servers:
Messaging
* Social notifications
* Events
* Miscellaneous servers:
Jenkins, monitoring, bastion hosts, security scanners
* Business Requirements
* Build a reliable and reproducible environment with scaled parity of production. Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud. Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud. Support failover of the production environment to cloud during an emergency. Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model.
For this question, refer to the Dress4Win case study.
You want to ensure Dress4Win's sales and tax records remain available for infrequent viewing by auditors for at least 10 years. Cost optimization is your top priority. Which cloud services should you choose?
- A. BigQuery to store the data, and a web server cluster in a managed instance group to access the data. Google Cloud SQL mirrored across two distinct regions to store the data, and a Redis cluster in a managed instance group to access the data.
- B. Google Bigtabte with US or EU as location to store the data, and gcloud to access the data.
- C. Google Cloud Storage Coldline to store the data, and gsutil to access the data.
- D. Google Cloud Storage Nearline to store the data, and gsutil to access the data.
Answer: C
Explanation:
References: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION 29
Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small services that they want to be able to update and roll back quickly. Mountkirk Games has the following requirements:
* Services are deployed redundantly across multiple regions in the US and Europe
* Only frontend services are exposed on the public internet
* They can provide a single frontend IP for their fleet of services
* Deployment artifacts are immutable
Which set of products should they use?
- A. Google Cloud Storage, Google App Engine, Google Network Load Balancer
- B. Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine
- C. Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer
- D. Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager
Answer: C
NEW QUESTION 30
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