A digital learning platform is launching a real-time examination service. During school ex… — AWS Certified Solutions Architect - Professional

A digital learning platform is launching a real-time examination service. During school exams, the platform experiences a sudden surge in traffic. Because exams are scheduled independently by thousands of schools throughout the day, the spikes are frequent but unpredictable at the global platform level, making manual pre-warming requests impractical. When an exam starts, submission traffic spikes from $50\text{ requests/second}$ to $35,000\text{ requests/second}$ within $15\text{ seconds}$ . The system must process these submissions, save them to a relational database, and immediately update a real-time monitor dashboard for instructors. The dashboard queries are highly concurrent and read-heavy, requiring sub- $50\text{ ms}$ latency. Which architectural design will meet these scalability and performance requirements while preventing dropped requests?

Deploy a Network Load Balancer (NLB) to route incoming traffic to the application tasks. Store the data in an Amazon Aurora PostgreSQL DB cluster. Create an Aurora Auto Scaling policy for Aurora Replicas to scale out the read capacity of the cluster, and configure the dashboard to query the Aurora reader endpoint.Cevap
B
Deploy a Network Load Balancer (NLB) to route incoming traffic to the application tasks. Store the data in an Amazon RDS for PostgreSQL DB instance in a Multi-AZ deployment. Configure the dashboard to query the secondary standby DB instance in the secondary Availability Zone to offload read traffic from the primary instance.
C
Deploy an Application Load Balancer (ALB) to route incoming traffic to the application tasks. Store the data in an Amazon RDS for PostgreSQL DB instance in a Multi-AZ deployment. Configure the dashboard to query the secondary standby DB instance using its node-specific DNS endpoint to distribute the read-heavy queries.
D
Deploy an Application Load Balancer (ALB) to route incoming traffic to the application tasks. Store the data in an Amazon Aurora PostgreSQL DB cluster. Configure a target tracking scaling policy on the ALB to scale the database writer instance size vertically using AWS Lambda when CPU utilization exceeds $70\%$ .

Cevap

Deploy a Network Load Balancer (NLB) to route incoming traffic to the application tasks. Store the data in an Amazon Aurora PostgreSQL DB cluster. Create an Aurora Auto Scaling policy for Aurora Replicas to scale out the read capacity of the cluster, and configure the dashboard to query the Aurora reader endpoint.

The correct design uses a Network Load Balancer (NLB) to handle the sudden, extreme traffic spike without pre-warming, ensuring that no requests are dropped. It also scales the relational database tier horizontally using Amazon Aurora PostgreSQL Replicas configured with Auto Scaling. Read traffic is routed through the reader endpoint, which distributes queries across the replicas to maintain sub-

50\text{ ms}

latency for the dashboard.

Adım Adım Çözüm

Analyze the ingress requirements for the flash traffic spike.

Identify that traffic increases from

50\text{ requests/second}

35,000\text{ requests/second}

within

15\text{ seconds}

(a factor of

700\times

increase). Because the spikes are unpredictable globally, manual pre-warming of an Application Load Balancer is not operationally feasible. A Network Load Balancer must be selected because it can handle sudden, volatile traffic spikes of this magnitude without pre-warming.

Choosing the correct load balancer prevents dropped requests during rapid scale-up events.

Evaluate the database requirements for high-concurrency reads and writes.

Identify that the system needs to support both raw submission writes and highly concurrent dashboard read queries with sub-

50\text{ ms}

latency.

This determines the partitioning and scaling strategy for the database layer.

Select the database technology and scaling mechanism.

Choose Amazon Aurora PostgreSQL with Aurora Replicas. Implement Aurora Auto Scaling to dynamically adjust the number of read replicas, and configure the dashboard application to point to the reader endpoint.

This allows read traffic to scale out horizontally to meet the performance and latency requirements under load.

Assess and reject invalid database read-scaling methods.

Identify that Amazon RDS Multi-AZ standby instances are passive and cannot be queried for read scaling. Any architecture directing read queries to the standby instance must be rejected.

This eliminates incorrect distractors that confuse high availability with read scalability.

Anahtar Kavram

Handling unpredictable flash traffic spikes at the load balancer tier using Network Load Balancers, and scaling read-heavy relational database workloads horizontally using Amazon Aurora Replicas with Auto Scaling rather than relying on passive RDS Multi-AZ standby instances.

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