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| Section | Objectives |
|---|---|
| Topic 1: Designing and planning a cloud solution architecture | - Designing solution infrastructure that meets business requirements
|
| Topic 2: Managing implementation | - Interacting with Google Cloud programmatically
|
| Topic 3: Managing and provisioning a solution infrastructure | - Configuring individual storage systems
|
| Topic 4: Analyzing and optimizing technical and business processes | - Analyzing and defining business processes
|
| Topic 5: Designing for security and compliance | - Designing for security
|
| Topic 6: Ensuring solution and operations reliability | - Evaluating quality control measures - Monitoring/logging/profiling/alerting solution
|
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NEW QUESTION # 97
A small number of API requests to your microservices-based application take a very long time.
You know that each request to the API can traverse many services. You want to know which service takes the longest in those cases. What should you do?
Answer: A
Explanation:
https://cloud.google.com/trace/docs/overview
NEW QUESTION # 98
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.
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?
Answer: D
NEW QUESTION # 99
Case Study: 12 - Altostrat Media
Company Overview
Altostrat is a prominent player in the media industry, with an extensive collection of audio and video content that comprises podcasts, interviews, news broadcasts, and documentaries. Their success in delivering premium content to a diverse audience requires a content management system that can keep pace with the dynamic media landscape.
Solution Concept
Altostrat seeks to modernize its content management and user engagement strategies using Google Cloud's generative AI. They want a platform that empowers customers with personalized recommendations, natural language interactions and seamless self-service support.
Simultaneously, they want to drive revenue growth through dynamic pricing targeted marketing, and personalized product suggestions.
The seamless integration of AI-powered tools into the existing Google Cloud environment will enable Altostrat to efficiently manage their vast media library, enhance user experiences, and unlock new revenue streams. Google Cloud's generative AI will solidify their leadership in the media industry.
Existing Technical Environment
Altostrat's content management and delivery platform leverages GKE for scalability and high availability, essential for handling their vast media library. Their extensive media library spanning various documents, audio and video formats is stored in Cloud Storage. To gain valuable insights into user behavior, content consumption patterns, and audience demographics, Altostrat leverages BigQuery as their primary data warehouse. Additionally, they use Cloud Run functions for serverless execution of event-driven tasks such as video transcoding metadata extraction, and personalized content recommendations.
While Altostrat has made significant strides in cloud adoption, they also maintain some legacy on- premises systems for specific workflows like content ingestion and archival. These systems are slated for modernization and migration to Google Cloud in the near future. User management and authentication are currently handled through a combination of Google Identity and third-party identity providers. For monitoring and observability, Altostrat relies on a mix of native Google Cloud tools like Cloud Monitoring and open-source solutions like Prometheus, with alerts primarily delivered via email notifications.
Business Requirements
- Accelerate and enhance the reliability of operational workflows across all environments. [Google
Cloud + On-premises]
- Simplify infrastructure management for rapid application deployment.
- Optimize cloud storage costs while maintaining high availability and scalability for media
content.
- Enable natural language interaction with the platform with 24/7 user support.
- Automatically generate concise summaries of media content.
- Extract rich metadata from media assets using NLP and computer vision.
- Detect and filter inappropriate content.
- Analyze media content to identify trends and extract insights.
- Inform content strategy and decision making with data.
Technical Requirements
- Modernize CI/CD for containerized deployments with a centralized management platform.
- Secure, high-performance hybrid cloud connectivity for data ingestion.
- Provide scalable, performant kubernetes environments both on-premises and in the cloud.
- Optimize cloud storage costs for growing media volumes.
- Design AI-powered detection of harmful content.
- Ensure that AI systems are auditable and their decisions can be explained.
- Leverage LLMs and conversational AI for personalized experiences and content virality.
- Develop advanced chatbots with natural language understanding to provide personalized
assistance.
- Automated summarization for diverse media.
Executive Statement
At Altostrat, we are embracing the next frontier of artificial intelligence to revolutionize our content strategy. By harnessing the power of generative AI, we will create an unparalleled user experience by empowering our audience with intelligent toots for content discovery, personalized recommendations, and seamless interaction. Reliability and cost management are our top priorities. This strategic initiative will deepen engagement, foster customer loyalty, and unlock new revenue streams through targeted marketing and tailored content offerings. We see a future where Al-driven innovation is central to our business, leading to greater success for our company and delivering exceptional value to our customers.
For this question, refer to the Altostrat Media case study. Altostrat is concerned about sophisticated, multi-vector Distributed Denial of Service (DDoS) attacks targeting various layers of their infrastructure. DDoS attacks could potentially disrupt video streaming and cause financial losses. You need to mitigate this risk. What should you do?
Answer: A
Explanation:
Google Cloud Armor provides managed, always-on protection against large-scale and multi- vector DDoS attacks at both the network layer and application layer. Its preconfigured protections and custom rules are designed specifically to defend internet-facing services like video streaming from volumetric, protocol, and application-layer attacks.
NEW QUESTION # 100
Your company has an application running as a Deployment in a Google Kubernetes Engine (GKE) cluster When releasing new versions of the application via a rolling deployment, the team has been causing outages The root cause of the outages is misconfigurations with parameters that are only used in production You want to put preventive measures for this in the platform to prevent outages What should you do?
Answer: C
Explanation:
This option can help prevent outages caused by misconfigurations with parameters that are only used in production. Liveness and readiness probes are mechanisms to check the health and availability of the Pods and containers in a GKE cluster. Liveness probes determine if a container is still running, and if not, restart it.
Readiness probes determine if a container is ready to serve requests, and if not, remove it from the load balancer. By configuring liveness and readiness probes in the Pod specification, you can ensure that your application can handle traffic and recover from failures gracefully during a rolling update. The other options are not optimal for this scenario, because they either do not prevent outages, but only alert or monitor them (B, C), or do not apply to GKE clusters, but to Compute Engine instances (D). References:
https://cloud.google.com/kubernetes-engine/docs/how-to/updating-apps
https://cloud.google.com/blog/products/containers-kubernetes/kubernetes-best-practices-setting-up-health
NEW QUESTION # 101
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 are asked to design a new architecture for the ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow Google-recommended practices.
Considering the technical requirements, which components should you use for the ingestion of the data?
Answer: A
NEW QUESTION # 102
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