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| Section | Objectives |
|---|---|
| Topic 1: Ensuring solution and operations reliability | - Deployment and release management
- Assisting with support
|
| Topic 2: Designing for security and compliance | - Designing for security
|
| Topic 3: Designing and planning a cloud solution architecture | - Enabling future cloud technology
|
| Topic 4: Managing implementation | - Interacting with Google Cloud programmatically
|
| Topic 5: Analyzing and optimizing technical and business processes | - Analyzing and defining technical processes
|
| Topic 6: Managing and provisioning a solution infrastructure | - Configuring network topologies
|
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NEW QUESTION # 345
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?
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION # 346
Your company is planning to upload several important files to Cloud Storage. After the upload is completed, they want to verify that the upload content is identical to what they have on- premises. You want to minimize the cost and effort of performing this check. What should you do?
Answer: B
Explanation:
https://cloud.google.com/storage/docs/gsutil/commands/hash
NEW QUESTION # 347
Your company just finished a rapid lift and shift to Google Compute Engine for your compute needs. You have another 9 months to design and deploy a more cloud-native solution. Specifically, you want a system that is no-ops and auto-scaling.
Which two compute products should you choose? Choose 2 answers.
Answer: B,E
Explanation:
Explanation/Reference:
Explanation:
B: With Container Engine, Google will automatically deploy your cluster for you, update, patch, secure the nodes.
Kubernetes Engine's cluster autoscaler automatically resizes clusters based on the demands of the workloads you want to run.
C: Solutions like Datastore, BigQuery, AppEngine, etc are truly NoOps.
App Engine by default scales the number of instances running up and down to match the load, thus providing consistent performance for your app at all times while minimizing idle instances and thus reducing cost.
Note: At a high level, NoOps means that there is no infrastructure to build out and manage during usage of the platform. Typically, the compromise you make with NoOps is that you lose control of the underlying infrastructure.
References: https://www.quora.com/How-well-does-Google-Container-Engine-support-Google-Cloud- Platform%E2%80%99s-NoOps-claim
NEW QUESTION # 348
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?
Answer: D
Explanation:
Topic 5, Dress4Win Case 2
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. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated 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 moving their development and test environments. They are also 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. All servers run Ubuntu LTS v16.04.
Databases:
MySQL. 1 server for user data, inventory, static data:
- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
Redis 3 server cluster for metadata, social graph, caching. Each server is:
- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
40 Web Application servers providing micro-services based APIs and static content.
- Tomcat - Java
- Nginx
- 4 core CPUs
- 32 GB of RAM
20 Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUS
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
3 RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM
Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN - MySQL databases
- 1 PB total storage; 400 TB available
NAS - image storage, logs, backups
- 100 TB total storage; 35 TB available
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.
Technical Requirements
Easily create non-production environment in the cloud.
Implement an automation framework for provisioning resources in cloud.
Implement a continuous deployment process for deploying applications to the on-premises datacenter or cloud.
Support failover of the production environment to cloud during an emergency.
Encrypt data on the wire and at rest.
Support multiple private connections between the production data center and cloud environment.
Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
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 for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
NEW QUESTION # 349
Your organization has a significant amount of log data stored in Cloud Logging. The data engineering team is accustomed to using SQL for analysis and wants the ability to create insightful dashboards for visualizing log trends and patterns. You want to follow the recommendations of the Google Cloud Well-Architected Framework to provide a solution for the data engineering team What should you do?
Answer: A
Explanation:
The Google Cloud Well-Architected Framework recommends using specialized tools for specialized tasks.
* Log Analytics: This feature allows you to query your Cloud Logging data using Standard SQL directly in the console.
* BigQuery & Looker Studio: By linking the log bucket to BigQuery, your data engineering team can use their existing SQL skills to perform complex joins. Looker Studio (Option A) is then used to create high-level visualizations that are far more advanced than basic Cloud Monitoring charts.
* Why not D? While Pub/Sub to BigQuery works, it is more complex to manage ( " operational complexity " ) than simply enabling the built-in Log Analytics feature.
NEW QUESTION # 350
......
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