SAP-C02 Design for New Solutions Practice Question

A media company is designing a new application that ingests large video files uploaded by users. The files must be stored durably and then processed by a compute fleet that runs for several hours per file. The company wants to minimize compute costs and ensure that processing can resume if interrupted. Which architectural approach should a solutions architect recommend?

⚠ Common exam trap

The trap here is assuming that AWS Lambda or Fargate can handle multi-hour processing without considering their timeout limits or the lack of built-in batch orchestration.

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

✓

Store files in Amazon S3 and use AWS Batch with managed compute environments to run processing jobs.

AWS Batch is purpose-built for batch computing, supporting long-running jobs and managed compute environments that can use Spot Instances to reduce costs. It provides job queues, retries, and dependency management, enabling processing to resume after interruptions. Storing files in Amazon S3 ensures durability and accessibility for the compute fleet. This combination meets the requirements for cost minimization and resiliency.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Store files in Amazon S3 and use Amazon ECS with Fargate tasks that run for up to 24 hours.
Why it's wrong here
Amazon ECS with Fargate has a maximum task timeout of 24 hours, but it is not optimized for batch processing with job queues and automatic retries. While it can run long tasks, managing the orchestration, retries, and cost optimization (e.g., Spot) requires additional effort. AWS Batch provides a more suitable managed batch environment with built-in job scheduling and compute resource management.
✗
Store files in Amazon S3 and use AWS Lambda with a 15-minute timeout to process each file.
Why it's wrong here
AWS Lambda has a maximum timeout of 15 minutes, which is insufficient for processing that takes several hours. While Lambda can be triggered by S3 events, the execution time limit makes it unsuitable for long-running video processing. The solution would fail for files requiring hours of compute, and there is no built-in checkpointing to resume after interruption.
✓
Store files in Amazon S3 and use AWS Batch with managed compute environments to run processing jobs.
Why this is correct
AWS Batch is designed for batch computing workloads and can run jobs for hours or days. It manages compute resources dynamically, which can reduce costs by using Spot Instances and scaling down when not needed. AWS Batch also supports job queues and retries, allowing processing to resume if interrupted, and integrates with S3 for input and output.
✗
Store files in Amazon EBS and use a single large EC2 instance to process files sequentially.
Why it's wrong here
Amazon EBS volumes are not shared storage for multiple instances and are limited to a single Availability Zone. Using a single EC2 instance creates a single point of failure and does not scale. If the instance is interrupted, processing stops and cannot resume automatically. This approach also does not minimize costs because the instance runs continuously.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

SAP-C02 Design for New Solutions Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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