A financial technology company is designing a cost-effective compute environment for a ris… — AWS Certified Solutions Architect - Associate

A financial technology company is designing a cost-effective compute environment for a risk-modeling suite. The workload has the following components:
1. A core trading microservice running on Amazon ECS with AWS Fargate that requires a constant baseline of $10$ tasks running $24/7$ to handle routine API requests.
2. A continuous, long-running telemetry processing agent that must run $24/7$ to collect system metrics and process them in real time.
3. A large-scale simulation engine running on Amazon EC2 instances. The simulation runs batch jobs that are fault-tolerant, can be interrupted, and can resume from checkpoints.
4. A critical nightly database reconciliation job running on Amazon EC2 that runs for exactly $2$ hours starting at midnight and must complete without interruption.

Which combination of compute hosting configurations and purchasing strategies will meet these requirements most cost-effectively? (Select TWO.)

Purchase a Compute Savings Plan to cover the baseline capacity of the ECS Fargate microservice tasks.Answer
Run the EC2 simulation engine on Spot Instances, and run the nightly reconciliation job on On-Demand Instances.Answer
C
Deploy the continuous telemetry processing agent on AWS Lambda to benefit from the serverless pricing model.
D
Purchase a Compute Savings Plan to cover the baseline capacity of the Amazon Aurora database cluster.
E
Purchase an EC2 Instance Savings Plan to cover the baseline compute capacity of the ECS Fargate microservice tasks.

Answer

The most cost-effective compute strategy is to purchase a Compute Savings Plan for the ECS Fargate microservice baseline and to run the EC2 simulation engine on Spot Instances while running the nightly reconciliation job on On-Demand Instances.

Purchasing a Compute Savings Plan to cover the baseline capacity of the ECS Fargate tasks is correct because Compute Savings Plans apply directly to Fargate and offer deep discounts for predictable, continuous compute usage. Running the EC2 simulation engine on Spot Instances is correct because the simulation is fault-tolerant and can resume from checkpoints, allowing it to leverage Spot pricing (up to

90\%

savings). Running the nightly reconciliation job on On-Demand Instances is correct because it is a critical, non-interruptible process that runs for only

2

hours daily, meaning it does not justify a

1

-year or

3

-year savings commitment and cannot safely run on Spot.

Step-by-Step Solution

Analyze the pricing model and suitability of Compute Savings Plans for Fargate tasks.

Compute Savings Plans apply automatically to ECS Fargate usage, offering significant discounts for predictable, continuous

24/7

workloads.

Determining the most cost-effective way to run the core microservice baseline.

Evaluate compute purchasing options for the simulation engine and the reconciliation job.

The simulation engine is fault-tolerant and can resume from checkpoints, making it ideal for Spot Instances. The critical reconciliation job is short-running (

2

hours daily) and cannot be interrupted, making On-Demand Instances the best choice.

Matching workload fault tolerance and duration to the appropriate EC2 pricing model.

Identify scope limitations of savings plans and compute hosting models for database and telemetry components.

Compute Savings Plans do not apply to Amazon Aurora, and running a continuous

24/7

agent on AWS Lambda is financially inefficient due to invocation limits.

Eliminating incorrect distractor strategies that violate AWS service limits and savings plan scopes.

Key Concept

Matching AWS compute workloads to optimal purchasing models (Savings Plans, Spot, and On-Demand) based on availability, continuity, and service coverage rules.

Question

Answer

Step-by-Step Solution

Key Concept

Common Mistakes

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