Cloud Application: Availability and Scale

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Questions and Answers

Consider a latency-sensitive application with strict uptime requirements deployed across multiple Azure regions. If a regional outage occurs, which of the following strategies would MOST effectively minimize downtime and data loss, assuming asynchronous data replication is in place?

Rely solely on Azure's automatic failover mechanisms without any pre-configured manual intervention processes to minimize complexity and potential human error.
Utilize a combination of Azure Traffic Manager and Azure Site Recovery, prioritizing rapid DNS propagation and leveraging pre-replicated virtual machines in the secondary region for near-instantaneous failover. (correct)
Initiate a manual failover to a pre-configured secondary region, ensuring data consistency by temporarily suspending write operations during the switchover period.
Employ a multi-master database architecture with immediate data synchronization across all regions to ensure zero data loss and continuous availability, despite increased costs.

An organization is refactoring a monolithic application into microservices and deploying them on Azure Kubernetes Service (AKS). To optimize resource utilization and minimize costs, which scaling strategy should be implemented, considering the varying and unpredictable load patterns of individual microservices?

Manually scale the number of pods for each microservice during peak hours and reduce them during off-peak hours based on historical data and predefined schedules.
Implement vertical scaling for each microservice by dynamically adjusting the CPU and memory allocation of the underlying virtual machines based on aggregated cluster-wide metrics.
Use Horizontal Pod Autoscaler (HPA) for each microservice, configured with custom metrics and scaling policies that consider both CPU utilization and application-specific performance indicators. (correct)
Employ Cluster Autoscaler to dynamically adjust the number of nodes in the AKS cluster based on the overall resource demand across all microservices, coupled with default CPU-based HPA settings.

A global e-commerce company aims to deploy its platform on Azure, ensuring both high availability and optimal performance for users worldwide. Considering potential network latency issues and regional traffic spikes, which deployment architecture would be MOST suitable?

Implement a geo-distributed architecture using Azure Front Door, deploying active-active application instances across multiple Azure regions and leveraging intelligent traffic routing based on latency and health. (correct)
Deploy the entire application stack in a single Azure region with a robust content delivery network (CDN) to cache static assets and distribute them globally.
Utilize Azure Traffic Manager with a priority-based routing method, directing all traffic to the primary region and failing over to a secondary region only in case of a complete outage.
Deploy separate application instances in each geographic region, manually redirecting users to their nearest region based on IP address using a custom DNS solution.

In the context of designing a highly resilient cloud application on Azure, which of the following approaches to handling transient faults (e.g., temporary network interruptions, service unavailability) would be MOST effective in minimizing user impact and ensuring continuous operation?

<p>Implement a circuit breaker pattern with exponential backoff and retry mechanisms at the application level, combined with appropriate logging and monitoring to detect and address recurring issues. (C)</p> Signup and view all the answers

A financial institution requires an Azure-based solution for processing high-volume, time-sensitive transactions with strict regulatory compliance requirements. Which combination of Azure services and architectural patterns would BEST ensure data integrity, auditability, and minimal latency, while also providing robust scalability and disaster recovery capabilities?

<p>Employ Azure SQL Database with geo-replication and auto-failover groups, combined with Azure Logic Apps for orchestrating transaction workflows and Azure Key Vault for secure key management. (D)</p> Signup and view all the answers

Flashcards

High Availability

Ensuring maximum uptime and accessibility, even when disruptions occur.

Service-Level Agreements (SLAs)

Legal agreements by service providers to maintain specific service uptime and performance levels.

Scalability

Adjusting resources to meet fluctuating demand.

Vertical Scaling

Increasing/decreasing the power (CPU, RAM) of a single resource.

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Horizontal Scaling

Adding/removing the number of resources to handle demand.

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Study Notes

Uptime (availability) and the ability to handle demand (scale) are critical when building or deploying a cloud application.
High availability ensures maximum availability, irrespective of disruptions.
Azure is a highly available environment with uptime guarantees detailed in its service-level agreements (SLAs).
The scalability of cloud resources is a major benefit of cloud computing.
Scalability is the ability to adjust resources to meet demand.
The cloud operates on a consumption-based model, meaning users pay only for the resources they use.
Scaling includes vertical and horizontal scaling.
Vertical scaling involves increasing or decreasing the capabilities of resources
Horizontal scaling involves adjusting the number of resources.
Vertical scaling can increase processing power by adding CPUs or RAM to a virtual machine or reduce costs by lowering CPU or RAM specifications.
Horizontal scaling allows for scaling out by adding virtual machines or containers during high demand, or scaling in to reduce resources when demand drops.

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Description

Uptime and demand management are essential for cloud applications. Azure ensures high availability with detailed SLAs. Cloud scalability allows resource adjustment based on demand, following a consumption-based model.