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Difficulty: HardAWS Compute Services

A logistics company is designing an AWS architecture for two distinct workloads. The first workload is a containerized microservices API for processing real-time order transactions; it experiences highly variable traffic, and the company wants to run it without managing the underlying servers. The second workload is an on-demand batch analytics job that processes historical shipping records, takes approximately 4 hours to complete, requires a customized Linux environment with proprietary software, and can be paused or restarted without impact if interrupted. Which two of the following compute options should the company select to meet these requirements? (Select two.)

  1. AWS Fargate to deploy the containerized microservices API without managing EC2 instancesAnswer
  2. Amazon EC2 Spot Instances to run the batch analytics jobAnswer
  3. C
    AWS Lambda to run the batch analytics job as a serverless function
  4. D
    Amazon EC2 Reserved Instances to run the batch analytics job

Answer

The correct answers are the option to use AWS Fargate to deploy the containerized microservices API without managing EC2 instances, and the option to use Amazon EC2 Spot Instances to run the batch analytics job.
AWS Fargate is the ideal serverless compute option for running containers (ECS/EKS) when developers want to eliminate the operational overhead of provisioning, configuring, and scaling virtual machines. Amazon EC2 Spot Instances offer the most cost-effective pricing model for workloads that are tolerant of interruptions (such as batch data processing) and require custom operating systems or applications running beyond serverless time limits.

Step-by-Step Solution

1
Analyze the requirements for the first workload (microservices API).
Identify that the workload is containerized, has variable traffic, and requires zero server management (serverless containers).
AWS Fargate is a serverless compute engine for containers that fits these exact needs by removing the need to manage EC2 instances.
2
Analyze the requirements for the second workload (batch analytics job).
Identify that the job is on-demand, runs for 4 hours, requires a customized Linux environment with proprietary software, and can tolerate interruptions.
The 4-hour duration rules out serverless options like AWS Lambda (which has a 15-minute limit), and the requirement for custom OS/proprietary software points to Amazon EC2.
3
Determine the optimal EC2 purchasing option for the batch job.
Identify that because the job is interruption-tolerant, Amazon EC2 Spot Instances provide the most cost-effective pricing compared to Reserved or On-Demand Instances.
Spot Instances offer up to 90% savings for flexible, interruption-tolerant workloads.

Key Concept

Selecting the appropriate AWS compute service and purchasing model based on execution time limits, server management preferences, containerization requirements, and fault tolerance.
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Topics

AWS Compute Services

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