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Geo-redundancy is the best option for a company that needs to ensure data availability in multiple datacenters around the world. Geo-redundancy is the duplication of IT infrastructure and data across geographically dispersed locations, such as different regions or continents1. Geo-redundancy provides several benefits for cloud-based applications, such as:

• High availability: Geo-redundancy can improve the availability of cloud services by reducing the impact of regional outages, disasters, or network failures. If one datacenter becomes unavailable, the application can switch to another datacenter that has a copy of the data and continue to operate2.
• Performance: Geo-redundancy can enhance the performance of cloud services by reducing the latency and bandwidth consumption for users who are located far from the primary datacenter. By having datacenters closer to the users, the application can deliver faster and more consistent responses3.
• Compliance: Geo-redundancy can help the company comply with regulatory or legal requirements that mandate data sovereignty or data protection. By having datacenters in different jurisdictions, the company can store and process data according to the local laws and regulations4.

The other options are not as suitable as geo-redundancy for the company’s needs because:

• Auto-scaling: Auto-scaling is the ability of cloud services to automatically adjust the amount of resources allocated to an application based on the demand and workload. Auto-scaling can improve the scalability and cost-efficiency of cloud services, but it does not guarantee data availability in multiple datacenters5.
• Disaster recovery: Disaster recovery is the process of restoring the normal operation of an application after a disruption or failure. Disaster recovery can involve backup, restore, and failover mechanisms, but it does not necessarily require data replication across multiple datacenters.
• High availability: High availability is the degree to which an application can operate without interruption or downtime. High availability can be achieved by using redundant components, load balancing, and fault tolerance techniques, but it does not imply data distribution across multiple datacenters.

References:

• 1: Georedundancy - Open Telekom Cloud
• 2: Why geo-redundancy for cloud infrastructure is a ‘must have’
• 3: Use geo-redundancy to design highly available applications
• 4: Protect your cloud infrastructure with geo-redundancy - Data Centre Review
• 5: What is Auto Scaling? - Amazon Web Services
• : What is Disaster Recovery? - Microsoft Azure
• : What is High Availability? - IBM Cloud

Spot instances are cloud computing resources that are available at a lower price than the regular on-demand price, but can be interrupted and reclaimed by the cloud provider at any time1. Spot instances are ideal for batch jobs that have flexible completion times and can tolerate failures, as they can provide faster and cheaper computing power than regular instances2. Spot instances can also be combined with other pricing options, such as on-demand or reserved instances, to optimize the performance and cost of batch jobs3.

Implementing right-sizing is a technique of adjusting the size and type of cloud resources to match the actual needs and usage patterns of an application4. Right-sizing can help reduce the cost and improve the efficiency of cloud resources, but it does not necessarily make the batch job faster, as it depends on the workload and demand of the job.

Increasing CPU usage is a measure of how much processing power is being consumed by an application or a system. Increasing CPU usage can make the batch job faster, but it can also increase the cost and risk of overloading the system. Increasing CPU usage is not a solution by itself, but rather a consequence of using more or larger cloud resources.

Adding storage is a process of increasing the amount or capacity of data that can be stored in the cloud. Adding storage can help store more data or backup data for the batch job, but it does not directly affect the speed or cost of the batch job, as it depends on the type and performance of the storage service. References: CompTIA Cloud Essentials+ CLO-002 Study Guide, Chapter 5: Cloud Resource Management, pages 191-192.

Right-sizing compute resource instances is the process of matching instance types and sizes to workload performance and capacity requirements at the lowest possible cost. It’s also the process of identifying opportunities to eliminate or downsize instances without compromising capacity or other requirements, which results in lower costs and higher efficiency1. Right-sizing is a key mechanism for optimizing cloud costs, but it is often ignored or delayed by organizations when they first move to the cloud. They lift and shift their environments and expect to right-size later. Speed and performance are often prioritized over cost, which results in oversized instances and a lot of wasted spend on unused resources2.

Right-sizing compute resource instances is the best action that the analyst should consider to lower costs and improve efficiency, as it can help reduce the amount of resources and money spent on instances that operate at a fraction of the full processing capacity. Right-sizing can also improve the performance and reliability of the instances by ensuring that they have enough resources to meet the workload demands. Right-sizing is an ongoing process that requires continuous monitoring and analysis of the instance usage and performance metrics, as well as the use of tools and frameworks that can simplify and automate the right-sizing decisions1.

Consolidating into fewer instances, using spot instances, or negotiating better prices on the company’s reserved instances are not the best actions that the analyst should consider to lower costs and improve efficiency, as they have some limitations and trade-offs compared to right-sizing. Consolidating into fewer instances can reduce the number of instances, but it does not necessarily optimize the type and size of the instances. Consolidating can also introduce performance and availability issues, such as increased latency, reduced redundancy, or single points of failure3. Using spot instances can reduce the cost of instances, but it also introduces the risk of interruption and termination, as spot instances are subject to fluctuating prices and availability based on the supply and demand of the cloud provider4. Negotiating better prices on the company’s reserved instances can reduce the cost of instances, but it also requires a long-term commitment and upfront payment, which reduces the flexibility and scalability of the cloud environment5. References: Right Sizing - Cloud Computing Services; The 6-Step Guide To Rightsizing Your Instances - CloudZero; Consolidating Cloud Services: How to Do It Right | CloudHealth by VMware; Spot Instances - Amazon Elastic Compute Cloud; Reserved Instances - Amazon Elastic Compute Cloud.

 Disaster recovery is the process of restoring the normal operations of an organization after a disruptive event, such as a natural disaster, a cyberattack, or a human error. Disaster recovery involves the planning, preparation, and implementation of strategies and procedures to minimize the impact and duration of the disruption, and to ensure the continuity and availability of the critical functions and data of the organization1

RTO and RPO are two key metrics that are used to measure and evaluate the disaster recovery capabilities and objectives of an organization. RTO stands for Recovery Time Objective, which is the maximum acceptable amount of time that an application or a service can be offline or unavailable after a disruption. RPO stands for Recovery Point Objective, which is the maximum acceptable amount of data that can be lost or unrecoverable after a disruption2

When designing a new cloud-enabled application, an organization that is considering RTO and RPO is most likely concerned about disaster recovery, as these metrics can help the organization to determine the optimal level of backup, redundancy, and recovery for the application, as well as the potential costs and risks of downtime or data loss. RTO and RPO can also help the organization to choose the appropriate cloud service model, provider, and deployment option that can meet the disaster recovery requirements and expectations of the organization and its customers3

References: CompTIA Cloud Essentials+ Certification Exam Objectives4, CompTIA Cloud Essentials+ Study Guide, Chapter 7: Cloud Security5, Cloud Essentials+ Certification Training