CLF-C02 Cloud Technology and Services Practice Question
Which AWS compute service is best suited for running batch processing jobs that require specific compute resources and can run for hours or days without user interaction?
⚠ Common exam trap
A common mix-up: candidates confuse AWS Lambda's serverless model with batch processing, overlooking Lambda's hard 15-minute timeout and lack of support for long-running, resource-intensive workloads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Batch
AWS Batch is purpose-built for running batch computing workloads that can run for extended durations without user interaction. It dynamically provisions the optimal quantity and type of compute resources (e.g., EC2 instances or Spot Instances) based on the volume and specific resource requirements of the submitted jobs, making it ideal for hours- or days-long batch processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
AWS Lambda is a serverless, event-driven compute service designed for short, stateless functions. Its maximum invocation timeout is 15 minutes, and it has limitations on ephemeral storage and memory that preclude running batch workloads spanning hours or days. For long-running batch processing, you would need to split the job into many small functions and orchestrate state externally, which is impractical compared to purpose-built batch services.
- ✗
Amazon EC2 with manual provisioning
Why it's wrong here
Manually provisioning Amazon EC2 instances for batch jobs forces you to build and operate the entire job orchestration layer yourself: designing queues, writing schedulers, setting up retry logic, and implementing auto scaling based on job load. You also remain responsible for patching, monitoring instance lifecycle, and terminating idle resources, which diverts engineering effort away from the actual data processing. AWS Batch natively automates queue management, job scheduling, and instance lifecycle, eliminating this overhead.
- ✓
AWS Batch
Why this is correct
AWS Batch is a fully managed batch computing service that dynamically provisions the optimal quantity and type of compute resources—such as EC2 or Spot Instances—based on the volume of jobs submitted. It manages job queues, schedules jobs according to dependencies and priorities, automatically retries failed jobs, and scales resources up or down to meet demand, then terminates resources when processing completes. This makes it ideal for long-running, high-throughput batch workloads that can run for hours or even days without manual intervention.
- ✗
Amazon ECS with Fargate
Why it's wrong here
Amazon ECS with Fargate can run containerized batch jobs, but it provides no built-in job scheduling or queue management. You would have to implement your own job scheduler, maintain a queue service, handle retries and dependencies manually, and decide how to scale tasks based on pending work. In contrast, AWS Batch is purpose-built to manage job queues, scheduling, and automatic scaling for batch jobs, making it a more operationally efficient choice for this requirement.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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