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| Section | Objectives |
|---|---|
| Analyzing and optimizing technical and business processes | - Analyzing and defining technical processes
|
| Managing implementation | - Interacting with Google Cloud programmatically
|
| Designing for security and compliance | - Designing for compliance
|
| Designing and planning a cloud solution architecture | - Creating a migration plan
|
| Managing and provisioning a solution infrastructure | - Configuring individual storage systems
|
| Ensuring solution and operations reliability | - Assisting with support
|
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NEW QUESTION # 112
Case Study: 9 - Helicopter Racing League
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
- Existing content is stored in an object storage service on their existing public cloud provider.
- Video encoding and transcoding is performed on VMs created for each job.
- Race predictions are performed using TensorFlow running on VMs in the current public cloud
provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
- Support ability to expose the predictive models to partners.
- Increase predictive capabilities during and before races:
*Race results
*Mechanical failures
*Crowd sentiment
- Increase telemetry and create additional insights.
- Measure fan engagement with new predictions.
- Enhance global availability and quality of the broadcasts.
- Increase the number of concurrent viewers.
- Minimize operational complexity.
- Ensure compliance with regulations.
- Create a merchandising revenue stream.
Technical requirements
- Maintain or increase prediction throughput and accuracy.
- Reduce viewer latency.
- Increase transcoding performance.
- Create real-time analytics of viewer consumption patterns and engagement.
- Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
For this question, refer to the Helicopter Racing League (HRL) case study. Your team is in charge of creating a payment card data vault for card numbers used to bill tens of thousands of viewers, merchandise consumers, and season ticket holders. You need to implement a custom card tokenization service that meets the following requirements:
- It must provide low latency at minimal cost.
- It must be able to identify duplicate credit cards and must not store plaintext card numbers.
- It should support annual key rotation.
Which storage approach should you adopt for your tokenization service?
Answer: B
Explanation:
https://cloud.google.com/community/tutorials/pci-tokenizer
Deterministic output means that a given set of inputs (card number, expiration, and userID) will always generate the same token. This is useful if you want to rely on the token value to deduplicate your token stores. You can simply match a newly generated token to your existing catalog of tokens to determine whether the card has been previously stored. Depending on your application architecture, this can be a very useful feature. However, this could also be accomplished using a salted hash of the input values.
https://cloud.google.com/architecture/tokenizing-sensitive-cardholder-data-for-pci-dss Firestore is the next major version of Datastore. Firestore can run in Datastore mode, which uses the same API as Datastore and scales to millions of writes per second,
NEW QUESTION # 113
For this question, refer to the TerramEarth case study.
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?
Answer: D
Explanation:
Reference:
https://cloud.google.com/solutions/data-lifecycle-cloud-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform
NEW QUESTION # 114
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?
Answer: C
NEW QUESTION # 115
For this question, refer to the JencoMart case study.
The JencoMart security team requires that all Google Cloud Platform infrastructure is deployed using a least privilege model with separation of duties for administration between production and development resources.
What Google domain and project structure should you recommend?
Answer: D
Explanation:
Explanation
Note: The principle of least privilege and separation of duties are concepts that, although semantically different, are intrinsically related from the standpoint of security. The intent behind both is to prevent people from having higher privilege levels than they actually need Principle of Least Privilege: Users should only have the least amount of privileges required to perform their job and no more. This reduces authorization exploitation by limiting access to resources such as targets, jobs, or monitoring templates for which they are not authorized.
Separation of Duties: Beyond limiting user privilege level, you also limit user duties, or the specific jobs they can perform. No user should be given responsibility for more than one related function. This limits the ability of a user to perform a malicious action and then cover up that action.
References: https://cloud.google.com/kms/docs/separation-of-duties
Topic 4, Dress4Win case study
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model.
Company Background
Dress4win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location.
Databases:
MySQL - user data, inventory, static data
Redis - metadata, social graph, caching
Application servers:
Tomcat - Java micro-services
Nginx - static content
Apache Beam - Batch processing
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN - MySQL databases
NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
Data analysis
Real-time trending calculations
MQ servers:
Messaging
Social notifications
Events
Miscellaneous servers:
Jenkins, monitoring, bastion hosts, security scanners
Business Requirements
Build a reliable and reproducible environment with scaled parity of production.
Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud.
Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud.
Support failover of the production environment to cloud during an emergency.
Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model.
NEW QUESTION # 116
You have an App Engine application that needs to be updated. You want to test the update with production traffic before replacing the current application version.
What should you do?
Answer: B
Explanation:
Explanation
NEW QUESTION # 117
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