An IoT fleet management company operates a vehicle tracking platform on AWS. The platform'… — AWS Certified Solutions Architect - Associate

An IoT fleet management company operates a vehicle tracking platform on AWS. The platform's backend infrastructure includes:

1. A data ingestion API running $24/7$ on AWS Fargate to receive telemetry data from millions of active vehicles. This workload maintains a highly predictable, steady-state baseline of $32\text{ vCPUs}$ and $64\text{ GB RAM}$ .
2. A fleet command service running on Amazon EC2 instances. This service executes highly parallel, containerized tasks to send software updates back to vehicles. The tasks are short-lived, tolerant of interruptions, and triggered by sporadic queue-based events.
3. An Amazon Aurora PostgreSQL database cluster that stores telemetry data and device states.

Which two purchasing and compute strategies should a solutions architect recommend to optimize the system's costs? (Select TWO.)

Purchase a Compute Savings Plan to cover the steady-state baseline compute usage of the data ingestion API running on AWS Fargate.Answer
Use Spot Instances for the EC2 fleet command service to execute the fault-tolerant, short-lived tasks.Answer
C
Purchase a Compute Savings Plan to cover the DB instances within the Amazon Aurora PostgreSQL database cluster.
D
Migrate the data ingestion API to AWS Lambda with Provisioned Concurrency to run the continuous, steady-state telemetry ingestion without provisioning infrastructure.
E
Configure the EC2 instances for the fleet command service to use Dedicated Hosts with a $3$ -year Reservation.

Answer

The correct strategies are to purchase a Compute Savings Plan to cover the baseline AWS Fargate compute usage, and to use Spot Instances for the EC2-based fleet command service running short-lived, interruptible tasks.

Purchasing a Compute Savings Plan covers the baseline AWS Fargate data ingestion API, offering significant cost reduction for predictable compute usage. Using Spot Instances for the fleet command service on EC2 provides maximum savings for highly parallel, short-lived, and fault-tolerant tasks.

Step-by-Step Solution

Analyze the data ingestion API workload requirements.

The API runs

24/7

with a predictable, steady-state baseline of

32\text{ vCPUs}

and

64\text{ GB RAM}

on AWS Fargate.

Identifying the workload profile determines the best purchasing strategy. For Fargate compute with a steady baseline, Compute Savings Plans provide the maximum discount.

Analyze the fleet command service workload requirements.

The service runs highly parallel, containerized tasks on EC2 that are short-lived, triggered workloads, and tolerant of interruptions.

Since the workload is fault-tolerant, short-lived, and scales dynamically, Spot Instances are the most cost-effective option.

Evaluate the database tier cost optimization requirements.

The database is Amazon Aurora PostgreSQL.

Note that Compute Savings Plans do not cover RDS/Aurora DB instances, so any option suggesting this is incorrect.

Key Concept

Selecting the optimal compute hosting and purchasing models (Compute Savings Plans for baseline Fargate, Spot Instances for interruptible EC2 workloads) while recognizing the boundary limitations of AWS Savings Plans (does not apply to RDS/Aurora).

Question

Answer

Step-by-Step Solution

Key Concept

Common Mistakes

Topics